From 2a08226ba715de3d6877d29f31053e8799509fa9 Mon Sep 17 00:00:00 2001 From: Tarzelf Date: Sun, 18 Oct 2026 23:46:49 -0400 Subject: [PATCH] Publish native image-goal benchmark and explicit unrun Jev protocol --- docs/downloads/reports/Image goal Jev v01.md | 113 + .../2026-10-18-image-goal-jev-v01/lab/LICENSE | 21 + .../lab/README.md | 56 + .../lab/cases.json | 2580 ++++++ .../lab/evaluation-plan.json | 643 ++ ...3bfe520918d470fa2bec53ecf84a493aed0556.png | Bin 0 -> 69903 bytes ...db4f561ff42830e44d0c7ff25cd7ea59843957.png | Bin 0 -> 81396 bytes ...5b0fbdb9f6cda29847c287cbadf74d342b4c14.png | Bin 0 -> 87865 bytes ...a7c7f8f482324bd5002900affc445a0eeb8106.png | Bin 0 -> 62348 bytes ...b6999ad2c2b1cfc570daa1320b5724bf556fa8.png | Bin 0 -> 64921 bytes ...806a62229856edb4d17c489a02ea1fd777326a.png | Bin 0 -> 83583 bytes ...0fe6d615c880605cdd3ea89089495c641b77d2.png | Bin 0 -> 84090 bytes ...22049b103cdf547c446d6e8e5652abf499a236.png | Bin 0 -> 78104 bytes 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ready, and what remains unmeasured + +We have built and validated **32 native PiPER scene fixtures** for an image-goal experiment. Four pairs have byte-identical current images and different physical completion labels: a settled shoe and the same shoe with an assigned velocity. The images alone cannot distinguish those states. A useful decision layer must recognize when the available evidence is insufficient. + +**Jev has not been run.** A TypeSafe credential was unavailable for this execution. All 64 planned provider trials remain explicitly `not_run`, and model accuracy, false-completion rates, service latency and cost remain unmeasured. This publication contains the native benchmark, frozen Jev protocol and runnable evaluation tools. It reports no successful model evaluation or learned robot controller. + +[Inspect all current and goal images](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/) · [Frozen evaluation plan](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json) · [Explicit provider status](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json) + +## The visual goal and the physical goal + +The task is to leave the green shoe at a marked position and orientation, supported by the table and released by the robot. Completion requires every declared condition to hold at each native sample throughout the final 200 ms window. + +| Physical requirement | Frozen threshold | +| --- | ---: | +| Position error in the table plane | At most 25 mm | +| Yaw error | At most 15° | +| Linear speed | At most 0.01 m/s | +| Angular speed | At most 0.05 rad/s | +| Table support force | Greater than 0.01 N | +| Robot contact force | At most 0.01 N | +| Final qualifying window | 100 consecutive samples, 2 ms apart | + +This is a sampled simulation criterion, not a continuous-time or physical hardware certification. Native force, motion and pose measurements determine the physical labels; no image model participates in their construction. + +The PiPER profile uses its existing six arm joints and coupled parallel gripper. The object is a declared **30 g rigid proxy with half the packaged trainer's width**. Narrowing the object permits contact with the unchanged gripper for this diagnostic. It does not improve the full-size shoe results in our [previous graspability study](https://research.dvidia.org/papers/shoe-graspability-v03/), which remain unchanged. + +The model assets are pinned to MuJoCo Menagerie commit `0059d4335f8156206f63a35662313385f7ad6d74`. Ninety files, totaling 34,562,302 bytes, were verified against the existing lock before generation. The PiPER asset's MIT notice is retained in the evidence bundle. [Pinned upstream asset](https://github.com/google-deepmind/mujoco_menagerie/tree/0059d4335f8156206f63a35662313385f7ad6d74/agilex_piper). + +## Eight conditions, four layouts + +The cohort crosses eight authored conditions with four deterministic layouts and camera azimuths. These are correlated diagnostic fixtures, not 32 independent real-world tasks or an unseen-object generalization study. + +| Condition | Cases | Physical completion | Expected judgment from one RGB view | Expected judgment with native telemetry | +| --- | ---: | --- | --- | --- | +| Settled at the goal | 4 | Complete | Unknown | Complete | +| Target missing | 4 | Incomplete | Incomplete | Incomplete | +| Orange shoe in place of green target | 4 | Incomplete | Incomplete | Incomplete | +| Wrong position | 4 | Incomplete | Incomplete | Incomplete | +| Wrong orientation | 4 | Incomplete | Incomplete | Incomplete | +| Robot still in contact | 4 | Incomplete | Unknown | Incomplete | +| Moving, with the settled pose and pixels | 4 | Incomplete | Unknown | Incomplete | +| Completed target under an opaque cover | 4 | Complete | Unknown | Complete | + +Each fixture starts from an authored state. The unreleased condition uses a native approach and gripper closure; it does not demonstrate lifting. The moving condition assigns 0.12 m/s velocity without advancing time, deliberately preserving the settled pose and image. This is a declared state intervention, not a measured physical impulse or a completed manipulation episode. + +The four moving/settled pairs are the central negative control. Their current images and goal references match exactly, while the native velocity and completion labels differ. A single-image evaluator cannot recover an unobserved velocity from those identical inputs. Returning `unknown` is the intended behavior for both RGB-only cases. All four pairs also have identical complete RGB request hashes in the frozen evaluation plan; their telemetry requests differ. The RGB inputs do not leak the distinction through filenames or case categories. + +An image of visibly closed fingers also cannot establish the 0.01 N force threshold. The RGB-only label for the unreleased condition is therefore `unknown`. For an absent target, wrong identity or clear pose violation, a visible failed requirement is sufficient to answer `incomplete`. + +The generated cohort contains **8 physically complete and 24 physically incomplete cases**, with **8 absent-target cases** counting both missing objects and orange decoys. RGB-only evaluation has 16 known incomplete verdicts and 16 intended abstentions. The telemetry condition has 8 complete and 24 incomplete expected verdicts. + +## What we give Jev + +The experiment pins TypeSafe's experimental image-capable `jev-1.14.0`. Each scheduled request contains the task contract, current image, goal image and one of two input conditions. Three typed questions request target-presence probability, physical-completion probability and a `complete`, `incomplete` or `unknown` verdict. Presence is declared only at probability 0.8 or greater, absence at 0.2 or less; values between them abstain. These thresholds and prompts are frozen before service calls. [Image contract](https://docs.typesafe.ai/images), [model versions](https://docs.typesafe.ai/models). + +The two conditions are: + +- **RGB:** current and goal images with the task contract. +- **RGB plus telemetry:** the same images and task, with privileged simulator measurements of target presence, pose error, speed, contact forces, observation count and extrema across the full native window. + +The second condition deliberately supplies information that the image cannot establish. It includes oracle object presence and pose errors; it is not a demonstrated camera-based estimator. Its outcome would measure whether a decision service uses those supplied observations correctly. It would not demonstrate that Jev inferred force, speed or object identity from pixels. + +Ground-truth completion, expected verdicts and case-category names are excluded from model inputs. The complete native-window extrema preserve the evidence needed to evaluate the dwell rule, without substituting a precomputed completion label. The plan records semantic request hashes for all 64 paired trials in a deterministic shuffled order. + +## Measure errors without rewarding an outage + +The tools preserve every planned trial. There are no automatic retries or post-result changes to prompts, thresholds or case selection. An interrupted in-flight request remains marked for investigation; the public report refuses to export an unresolved run. + +| Measure | Meaning | +| --- | --- | +| Observability-aware accuracy | Expected `complete`, `incomplete` or `unknown` verdicts over the planned cohort | +| Known-verdict accuracy | Correct judgments on cases with a defined observable completion verdict | +| Justified abstention | `unknown` on cases whose inputs cannot establish completion | +| Physical false completion | Model says complete while the independent physical reference is incomplete | +| Missing-object false presence | Model says present when the target is physically absent | +| Coverage and errors | Explicit decisions, abstentions, failed requests and unrun cases, reported separately | +| Probability error | Completion-probability Brier score against physical labels, with its valid-response count | +| Latency | p50, p95, p99 and maximum wall time, with errors retained in the all-attempt distribution | +| Cost | Known-usage input-cost estimate and a separate count of attempts with unknown cost | + +False-completion counts retain all planned incomplete cases and also show the denominator of valid negative-case responses. This prevents a service outage from appearing to improve the model merely because fewer answers arrive. Selective physical accuracy is reported with its answered-only denominator and coverage; abstention is not counted as successful physical completion. + +Online timing starts with preloaded image bytes and includes request preparation, image upload, hosted response and parsing. It excludes fixture-file loading, the earlier rendering, a live camera, robot actuation and physical task duration. The client has a bounded socket-operation timeout; this is not a real-time deadline guarantee. Small-sample p99 values describe this diagnostic cohort and cannot establish production tail latency. + +## Native measurements and current limits + +The final local generation took **6.30 wall seconds** for the 32 fixtures, including native scene construction, state validation, RGB/segmentation rendering and export. This is a fixture-generation measurement, not Jev inference speed, raw physics throughput or robot reaction time. + +The native generator verifies each declared condition and the four byte-identical image pairs. Each physical record retains its model state, current measurement and native history. The complete case manifest also retains target visibility from MuJoCo geometry segmentation. This segmentation is ground-truth diagnostic evidence; no open-vocabulary detector or learned perception model was evaluated here. + +An [independent native audit](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json) recomputes all 32 physical labels, the sampled dwell rule and full-window telemetry extrema from recorded numbers. It verifies 67 original files, with eight physically complete cases and 24 incomplete cases. The [runnable verifier](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py) checks recorded evidence without calling Jev or running a new simulation. Its source hash is included in the audit; the reproduction guide explains how to extract the original fixture tree. + +The shoe remains rigid, friction and actuation remain simulated, and the camera is authored and calibrated inside the simulation. The cohort does not cover deformable footwear, unmodeled clutter, arbitrary goal viewpoints, unseen objects or real sensor failures. The fixed arm reaches and closes only to construct the unreleased fixture; no Jev answer enters the actuator path. + +## Run and inspect the benchmark + +The [native generator and evaluator](https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4) are frozen at `19acf0734d63ecebc07140c8d84302a54baee4c4`, based on the published graspability branch. The [publication source](https://github.com/Dvidia-Inference/dvidia-training/tree/3d3c8f660f6f9544d6a58c654bab143dd1eddc00) adds the viewer, independent verifier and publication tools without changing that core. Original DVIDIA code and authored fixture records use MIT. No Jev model weights or third-party training code are redistributed. + +[Current/goal viewer](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/) · [Every physical label](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json) · [Native protocol](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json) · [Complete package checksums](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json) · [Reproduction and licenses](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md) + +The original native inventory, viewer inventory and outer publication inventory remain separate. Frozen native files are copied without modification. Public provider records exclude raw service responses and credentials; no provider response exists for this unrun execution. + +Python 3.12+, the repository's `.[arm]` dependencies and the pinned PiPER assets are sufficient for local generation. The static viewer runs without a GPU, credential or server-side model. Hosted Jev evaluation additionally needs a TypeSafe account and network access. The source guide documents secure environment-variable or explicitly named Keychain credentials; tokens never belong in the public files. + +Jev output is evaluated here, not used for training or distillation. The model remains subject to TypeSafe's own terms. [Service agreement](https://typesafe.ai/legal/mca). + +## What this adds to Skillspace + +An installable skill needs a completion contract alongside its goal image. The record should preserve the image, object identity, expected pose, relevant contacts and the interval over which the state must hold. The decision layer can then distinguish a visible failure from a state that needs another observation. + +The immediate next measurement is the frozen Jev cohort once credentials are available. After that, the useful comparison is a local state-based verifier, a visual evaluator and the same evaluator with measured feedback. Physical skill execution remains a separate benchmark: choose a subgoal, move the arm under local limits, and verify the resulting state. This experiment prepares that boundary; it does not yet cross it. diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE new file mode 100644 index 0000000..a7056f2 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 DVIDIA contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md new file mode 100644 index 0000000..8d6b263 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md @@ -0,0 +1,56 @@ +# Image-goal verification: native benchmark and Jev protocol + +DVIDIA robotics research · 18 October 2026. + +**Jev has not been run.** No TypeSafe credential was available for this execution. All 64 planned provider trials remain `not_run`; model accuracy, false-completion rates, service latency and costs are unmeasured. The report does not turn unavailable inference into zero-error results. + +The completed local work generates 32 native PiPER scene fixtures: eight conditions across four deterministic layouts. Four moving/settled pairs have identical images and different physical completion labels. This is a controlled observation diagnostic, not a learned policy or hardware test. The rigid shoe proxy is deliberately narrow (half width) and 30 g; it is not a full-size calibrated shoe. + +Open `index.html` to compare current/goal images and all case labels offline. `cases.json` preserves privileged physical truth and the declared expected judgment for each input condition. `states/` preserves every authored state, measured native history and physical reference. `evaluation-plan.json` freezes 64 semantic requests, prompt/source/image hashes, scoring and no-retry rules before provider use. `results.json` preserves the explicit unrun rows while omitting raw provider response fields. + +`integrity.json` is the original unmodified native fixture inventory. `report-integrity.json` binds the offline viewer. `publication-integrity.json` binds the complete public package. These are distinct seals; none replaces an earlier inventory. + +## Reproduce + +Clone Dvidia-Inference/dvidia-training and check out publication commit `3d3c8f660f6f9544d6a58c654bab143dd1eddc00`. The native generator and evaluator remain frozen at core commit `19acf0734d63ecebc07140c8d84302a54baee4c4`; the later commit adds the viewer, publisher and independent verifier. Install Python 3.12+ and the repository's `.[arm]` extra. The PiPER model assets are fetched once by the existing robot profile setup and verified against the asset lock. Native simulation and viewer rendering then run locally. See `docs/image-goal-benchmark.md` in the source for complete commands, secure credential setup, model version and limits. + +The passive evaluator can be run with `python -m dvidia_training.image_goal_benchmark evaluate --fixtures PATH --output NEW_DIRECTORY`. It reads `TYPESAFE_API_KEY` or an explicitly named Keychain entry; never paste a token into a public report or source file. Hosted Jev requires network access and a TypeSafe account. The supplied benchmark data and static viewer run without a credential. This publication contains no model weights. + +## Independently check the native evidence + +`native-validation.json` records the completed independent audit of all 32 physical labels and full-window telemetry extrema. `verify-image-goal-fixtures.py` recomputes those values from the saved native records; it does not call Jev, render images, or run a new physical simulation. Its source hash is recorded in both the audit and publication provenance. + +After installing the pinned source above, extract only the original sealed fixture files to a fresh directory. From this downloaded package directory: + +```sh +python - <<'PYTHON' +import hashlib, json +from pathlib import Path, PurePosixPath +source = Path.cwd() +destination = source.parent / "image-goal-native-fixtures" +destination.mkdir(exist_ok=False) +inventory = json.loads((source / "integrity.json").read_text()) +for item in inventory["files"]: + relative = PurePosixPath(item["path"]) + assert not relative.is_absolute() and ".." not in relative.parts + data = (source / relative).read_bytes() + assert len(data) == item["bytes"] + assert hashlib.sha256(data).hexdigest() == item["sha256"] + target = destination / relative + target.parent.mkdir(parents=True, exist_ok=True) + target.write_bytes(data) +for name in ("integrity.json", "evaluation-plan.json"): + (destination / name).write_bytes((source / name).read_bytes()) +PYTHON +python verify-image-goal-fixtures.py --fixtures ../image-goal-native-fixtures --output ../native-validation-reproduced.json +``` + +The verifier intentionally rejects extra files inside the native fixture directory. Keeping the report and its publication metadata outside that directory preserves the original manifest boundary. Expected result: 32 checked cases, 8 physically complete, 24 incomplete, 67 original files verified. Host metadata in a reproduction can differ. + +## Licenses and scope + +Original DVIDIA code and authored synthetic fixture records: MIT (`LICENSE`). The PiPER simulation asset is from MuJoCo Menagerie commit `0059d4335f8156206f63a35662313385f7ad6d74`; its MIT notice is preserved in `licenses/agilex_piper/LICENSE`. No MuJoCo runtime or upstream mesh files are redistributed in this viewer. DVIDIA marks are brand identifiers; the code license grants no trademark rights. + +Jev is TypeSafe's hosted model and retains its own service terms. Its output is evaluated, not used to train or distill a DVIDIA policy. The two model-input conditions are RGB images alone and RGB plus privileged simulator measurements. The latter supplies object identity/presence, pose errors and complete native-window extrema; it is not a demonstrated real-camera or contact estimator. There is no direct motor-control path. + +[Research](https://research.dvidia.org/papers/image-goal-jev-v01/) · [Source](https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4) · [Hugging Face](https://huggingface.co/spaces/Dvidia/robot-simulation-lab) diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json new file mode 100644 index 0000000..9b4807c --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json @@ -0,0 +1,2580 @@ +{ + "schema": "dvidia.image-goal-scenes/v1", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "cases": [ + { + "id": "layout-01-complete", + "category": "complete", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-complete.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741727623932e-07, + "max": 1.0856805225155038e-07 + }, + "yaw_error_rad": { + "min": 2.196340269667258e-09, + "max": 2.1965561156353097e-09 + }, + "linear_speed_m_s": { + "min": 9.860075820885874e-10, + "max": 1.0577925091606234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627744970784057e-09, + "max": 2.046823336044342e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886606, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6192, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.51875, + 0.6578125, + 0.7625 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-missing_target", + "category": "missing_target", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-missing_target.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-01-wrong_object", + "category": "wrong_object", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_object.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-01-wrong_position", + "category": "wrong_position", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_position.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5699999772538076, + -0.10000010615852632, + -0.00010773874568926917 + ], + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.1699999772538407, + "max": 0.1699999772540272 + }, + "yaw_error_rad": { + "min": 2.1963402702132865e-09, + "max": 2.1965561161782934e-09 + }, + "linear_speed_m_s": { + "min": 9.860075879684076e-10, + "max": 1.057792509173234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747009029746e-09, + "max": 2.0468233362534212e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886605, + "max": 0.29429999900143505 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 7452, + "target_box_xyxy_normalized": [ + 0.5453125, + 0.6291666666666667, + 0.8, + 0.9041666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_orientation.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.40000010615852655, + -0.10000002274619275, + -0.00010773874568926942 + ], + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09, + "table_support_force_n": 0.2942999990014349, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741754859924e-07, + "max": 1.0856805256849158e-07 + }, + "yaw_error_rad": { + "min": 1.57079632459834, + "max": 1.5707963245985561 + }, + "linear_speed_m_s": { + "min": 9.860076309132707e-10, + "max": 1.057792509120737e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627746996178486e-09, + "max": 2.0468233360765137e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.2942999990014349 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 5574, + "target_box_xyxy_normalized": [ + 0.4375, + 0.5, + 0.63125, + 0.7791666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-unreleased", + "category": "unreleased", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-unreleased.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "supported": true, + "released": false, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.40145485082066407, + -0.09444160792298799, + -0.00010962509298383693 + ], + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149, + "table_support_force_n": 0.2995624074490855, + "robot_contact_force_n": 0.7619265340093823, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.00574563428980446, + "max": 0.005753664112566862 + }, + "yaw_error_rad": { + "min": 0.08437097640564, + "max": 0.08440615838439973 + }, + "linear_speed_m_s": { + "min": 9.643102940812748e-05, + "max": 9.969011014506973e-05 + }, + "angular_speed_rad_s": { + "min": 0.003462491733548149, + "max": 0.005988177262940627 + }, + "table_support_force_n": { + "min": 0.2995624074490855, + "max": 0.30103792478353864 + }, + "robot_contact_force_n": { + "min": 0.760598912244461, + "max": 0.7619265340093823 + } + } + }, + "visibility": { + "visible_target_pixels": 5914, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.5229166666666667, + 0.665625, + 0.75 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-moving_alias", + "category": "moving_alias", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-moving_alias.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.3624151236609657, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6192, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.51875, + 0.6578125, + 0.7625 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-occluded", + "category": "occluded", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-occluded.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741727623932e-07, + "max": 1.0856805225155038e-07 + }, + "yaw_error_rad": { + "min": 2.196340269667258e-09, + "max": 2.1965561156353097e-09 + }, + "linear_speed_m_s": { + "min": 9.860075820885874e-10, + "max": 1.0577925091606234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627744970784057e-09, + "max": 2.046823336044342e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886606, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-complete", + "category": "complete", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-complete.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741745389504e-07, + "max": 1.0856805243386722e-07 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007602226177e-10, + "max": 1.0577925091174393e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747344185066e-09, + "max": 2.0468233361619074e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866115, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6084, + "target_box_xyxy_normalized": [ + 0.3921875, + 0.5708333333333333, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-missing_target", + "category": "missing_target", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-missing_target.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-02-wrong_object", + "category": "wrong_object", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_object.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-02-wrong_position", + "category": "wrong_position", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_position.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5999999858101832, + -0.16000010763675523, + -0.00010773874568926928 + ], + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.1699999858102173, + "max": 0.1699999858103543 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007596264554e-10, + "max": 1.0577925091117024e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627749443387563e-09, + "max": 2.0468233361806336e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886612, + "max": 0.29429999900143505 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6701, + "target_box_xyxy_normalized": [ + 0.4765625, + 0.7229166666666667, + 0.7015625, + 1.0 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_orientation.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4300001076367553, + -0.16000001418981774, + -0.0001077387456892687 + ], + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674174082394e-07, + "max": 1.0856805246501234e-07 + }, + "yaw_error_rad": { + "min": 1.5707963245983363, + "max": 1.5707963245985526 + }, + "linear_speed_m_s": { + "min": 9.86007540976695e-10, + "max": 1.0577925091088337e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774938435085e-09, + "max": 2.04682333632664e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866126, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6498, + "target_box_xyxy_normalized": [ + 0.365625, + 0.5833333333333334, + 0.6, + 0.8458333333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-unreleased", + "category": "unreleased", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-unreleased.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.43040182038492686, + -0.1610479818214479, + -0.00010842273593493796 + ], + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277, + "table_support_force_n": 0.3014209771616913, + "robot_contact_force_n": 0.7693653082066636, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0010952390133150847, + "max": 0.00112237494618688 + }, + "yaw_error_rad": { + "min": 0.005609244269984187, + "max": 0.00569434757841164 + }, + "linear_speed_m_s": { + "min": 0.0001367889163867079, + "max": 0.00014160096890228054 + }, + "angular_speed_rad_s": { + "min": 0.0009941703968526277, + "max": 0.0017186694278268843 + }, + "table_support_force_n": { + "min": 0.3014209771616913, + "max": 0.3046279922938624 + }, + "robot_contact_force_n": { + "min": 0.7684190832360072, + "max": 0.7693653082066636 + } + } + }, + "visibility": { + "visible_target_pixels": 5816, + "target_box_xyxy_normalized": [ + 0.390625, + 0.5729166666666666, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-moving_alias", + "category": "moving_alias", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-moving_alias.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.36239906646539655, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6084, + "target_box_xyxy_normalized": [ + 0.3921875, + 0.5708333333333333, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-occluded", + "category": "occluded", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-occluded.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741745389504e-07, + "max": 1.0856805243386722e-07 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007602226177e-10, + "max": 1.0577925091174393e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747344185066e-09, + "max": 2.0468233361619074e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866115, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-complete", + "category": "complete", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-complete.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741726656525e-07, + "max": 1.0856805227409043e-07 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.860076492373484e-10, + "max": 1.0577925090833765e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774894432646e-09, + "max": 2.046823336032993e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886608, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6191, + "target_box_xyxy_normalized": [ + 0.4359375, + 0.4875, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-missing_target", + "category": "missing_target", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-missing_target.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-03-wrong_object", + "category": "wrong_object", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_object.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-03-wrong_position", + "category": "wrong_position", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_position.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5499999688429305, + -0.07000010400124504, + -0.00010773874568926812 + ], + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.16999996884296226, + "max": 0.1699999688431964 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.86007647555542e-10, + "max": 1.0577925090868942e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774718512132e-09, + "max": 2.0468233361579335e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 7062, + "target_box_xyxy_normalized": [ + 0.5703125, + 0.5583333333333333, + 0.828125, + 0.7666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_orientation.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3800001040012452, + -0.07000003115707004, + -0.00010773874568926854 + ], + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09, + "table_support_force_n": 0.29429999900143444, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741730644736e-07, + "max": 1.0856805243889215e-07 + }, + "yaw_error_rad": { + "min": 1.570796324598342, + "max": 1.570796324598559 + }, + "linear_speed_m_s": { + "min": 9.86007598072673e-10, + "max": 1.0577925090957944e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747152164955e-09, + "max": 2.0468233360100356e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866126, + "max": 0.29429999900143444 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 4876, + "target_box_xyxy_normalized": [ + 0.4703125, + 0.44375, + 0.625, + 0.7333333333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-unreleased", + "category": "unreleased", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-unreleased.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.38020236953882225, + -0.0693768704293019, + -9.617134314363833e-05 + ], + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283, + "table_support_force_n": 0.2639875277923961, + "robot_contact_force_n": 0.7863052928276089, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0006474789378581962, + "max": 0.0006551670719148867 + }, + "yaw_error_rad": { + "min": 0.003703847249796388, + "max": 0.003720938606997237 + }, + "linear_speed_m_s": { + "min": 0.00010986191904928065, + "max": 0.00014294490050929373 + }, + "angular_speed_rad_s": { + "min": 0.0024920357101732454, + "max": 0.0040346175968384084 + }, + "table_support_force_n": { + "min": 0.25922471284709236, + "max": 0.2640607875145524 + }, + "robot_contact_force_n": { + "min": 0.7857892493442301, + "max": 0.7878949274134518 + } + } + }, + "visibility": { + "visible_target_pixels": 5657, + "target_box_xyxy_normalized": [ + 0.4375, + 0.48541666666666666, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-moving_alias", + "category": "moving_alias", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-moving_alias.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.362350885628384, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6191, + "target_box_xyxy_normalized": [ + 0.4359375, + 0.4875, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-occluded", + "category": "occluded", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-occluded.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741726656525e-07, + "max": 1.0856805227409043e-07 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.860076492373484e-10, + "max": 1.0577925090833765e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774894432646e-09, + "max": 2.046823336032993e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886608, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-complete", + "category": "complete", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-complete.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674170840469e-07, + "max": 1.0856805211389336e-07 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.860076016164131e-10, + "max": 1.057792509081208e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627745907984584e-09, + "max": 2.046823336175752e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 5693, + "target_box_xyxy_normalized": [ + 0.3984375, + 0.5395833333333333, + 0.5796875, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-missing_target", + "category": "missing_target", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-missing_target.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-04-wrong_object", + "category": "wrong_object", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_object.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-04-wrong_position", + "category": "wrong_position", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_position.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09, + "table_support_force_n": 0.29429999900143494, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.16999999337338886, + "max": 0.16999999337348212 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.86007605782391e-10, + "max": 1.0577925090883427e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774742684285e-09, + "max": 2.0468233361281146e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.29429999900143494 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6447, + "target_box_xyxy_normalized": [ + 0.4578125, + 0.7020833333333333, + 0.6609375, + 1.0 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_orientation.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805194707237e-07, + "yaw_error_rad": 1.57079632459834, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007598437561e-10, + "angular_speed_rad_s": 2.7627747672417554e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4200001083656286, + -0.13000000662664574, + -0.0001077387456892685 + ], + "position_error_m": 1.0856805194707237e-07, + "yaw_error_rad": 1.57079632459834, + "linear_speed_m_s": 9.86007598437561e-10, + "angular_speed_rad_s": 2.7627747672417554e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741703541562e-07, + "max": 1.0856805194707237e-07 + }, + "yaw_error_rad": { + "min": 1.5707963245983396, + "max": 1.5707963245985561 + }, + "linear_speed_m_s": { + "min": 9.86007598437561e-10, + "max": 1.0577925091079773e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747672417554e-09, + "max": 2.0468233361383416e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6485, + "target_box_xyxy_normalized": [ + 0.359375, + 0.5645833333333333, + 0.6015625, + 0.8 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-unreleased", + "category": "unreleased", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-unreleased.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.0010491825564488413, + "yaw_error_rad": 0.03690074331083231, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00010064949189093267, + "angular_speed_rad_s": 0.002606933470747755 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.42104823526500446, + -0.13004457427461588, + -0.00011458521544506569 + ], + "position_error_m": 0.0010491825564488413, + "yaw_error_rad": 0.03690074331083231, + "linear_speed_m_s": 0.00010064949189093267, + "angular_speed_rad_s": 0.002606933470747755, + "table_support_force_n": 0.3144476609893772, + "robot_contact_force_n": 0.790406083741399, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0010350014788722895, + "max": 0.0010491825564488413 + }, + "yaw_error_rad": { + "min": 0.03690074331083231, + "max": 0.03691679299236298 + }, + "linear_speed_m_s": { + "min": 9.490361836146501e-05, + "max": 0.00010064949189093267 + }, + "angular_speed_rad_s": { + "min": 0.002606933470747755, + "max": 0.004742951667749043 + }, + "table_support_force_n": { + "min": 0.3142693700788355, + "max": 0.3148922546022384 + }, + "robot_contact_force_n": { + "min": 0.7881504132862833, + "max": 0.790406083741399 + } + } + }, + "visibility": { + "visible_target_pixels": 5582, + "target_box_xyxy_normalized": [ + 0.396875, + 0.5416666666666666, + 0.584375, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-moving_alias", + "category": "moving_alias", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-moving_alias.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.3623192350084462, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 5693, + "target_box_xyxy_normalized": [ + 0.3984375, + 0.5395833333333333, + 0.5796875, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-occluded", + "category": "occluded", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-occluded.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674170840469e-07, + "max": 1.0856805211389336e-07 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.860076016164131e-10, + "max": 1.057792509081208e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627745907984584e-09, + "max": 2.046823336175752e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + } + ], + "aliases": [ + { + "case_id": "layout-01-moving_alias", + "pixel_identical_to": "layout-01-complete" + }, + { + "case_id": "layout-02-moving_alias", + "pixel_identical_to": "layout-02-complete" + }, + { + "case_id": "layout-03-moving_alias", + "pixel_identical_to": "layout-03-complete" + }, + { + "case_id": "layout-04-moving_alias", + "pixel_identical_to": "layout-04-complete" + } + ], + "assets": { + "profile": "piper", + "repository": "https://github.com/google-deepmind/mujoco_menagerie", + "commit": "0059d4335f8156206f63a35662313385f7ad6d74", + "files_verified": 90, + "bytes_verified": 34562302, + "lock_sha256": "4b2ee2ca675aceb2f826983cb3082a3e08e9aa0ff419b23078abcff08ebcc6fe", + "simulation_asset_license": "MIT" + }, + "protocol_sha256": "d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4", + "mujoco_version": "3.15.0", + "rendering": "Native MuJoCo RGB and geom segmentation; no image-generation model.", + "generation_wall_seconds": 6.2972065838985145, + "scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator." +} diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json new file mode 100644 index 0000000..8e7c622 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json @@ -0,0 +1,643 @@ +{ + "automatic_retries": 0, + "cohort": "Authored diagnostic fixtures; four scene layouts are correlated clusters, not 32 independent real-world tasks.", + "completion_decision": "Unmodified Jev Choice verdict; unknown is abstention.", + "conditions": [ + "rgb", + "rgb_telemetry" + ], + "cost_scope": "Planning allowance assumes documented roughly 64k default context. 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DVIDIA
+

Robotics research · Image goals / 0.1

Does it only look finished?

A goal image describes the destination. This experiment asks Jev to judge the current scene, then compares its answer with the simulator’s physical state. Missing shoes and misleading arrangements stay in the results.

Loading the recorded experiment.

+

Current simulated scene
Current observation
Requested goal arrangement
Goal reference

Physical reference

Recorded simulator labels. A single image may not reveal hidden contact or sustained support.

Jev judgment

A remote research judgment. This page sends no images, runs no model and commands no robot.

+

Count the misses. Keep the clock honest.

Judgment accuracy uses the frozen, observability-aware verdict for every planned case: complete, incomplete, or unknown. A correct abstention matters when an image cannot establish hidden motion, contact or dwell. Errors and cases not run remain in the planned denominator; no score is claimed before requests are attempted. Physical false-completion and missing-target counts retain every planned negative case, with answered coverage shown separately.

Decision checkCorrectDenominator
Latency scopeTimed requestsp50p95p99Maximum

Timing is recorded wall-clock latency, not the robot’s reaction time or simulator timestep. Quantiles use linear interpolation and retain timed errors in the all-attempt row. A small cohort cannot establish production tail latency. Missing measurements remain unreported.

This benchmark uses synthetic simulation fixtures and a hosted decision model. It does not publish trained robot weights or demonstrate hardware transfer. The DVIDIA code license does not grant rights to the DVIDIA marks or to an external model.

Public results · JSONFile checksumsJev image documentation ↗
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b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE new file mode 100644 index 0000000..1ace5a2 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 RosenYin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json new file mode 100644 index 0000000..6a7ea49 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json @@ -0,0 +1,745 @@ +{ + "schema": "dvidia.image-goal-native-validation/v1", + "status": "passed", + "scope": "Independent recomputation from frozen native state measurements/history and raw coordinates. No images re-rendered, no fresh physics run, no model service called and no hardware validation.", + "fixture_generation_scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator.", + "source_digests": { + "protocol_sha256": "d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4", + "cases_sha256": "f8b1b205b16e9833df7bd7063e0572355794dcede49a024f5d73ae6f65bbb1aa", + "integrity_sha256": "e8b6bbe4f2928e85f6e88d16af7d719dc32b1a3b588ce9633805061528cbe682", + "verifier_sha256": "514be68c1dbd9ecb797f46615524f2bffa8654a5df3a62b90f4f8cc897414c08" + }, + "files_verified": 67, + "bytes_verified": 5019443, + "additional_runner_metadata": [ + { + "path": "evaluation-plan.json", + "bytes": 24662, + "sha256": "5f02006190d97bdaca43f139f5bf221cece23bba868a5e9ee0f424618ea87d8f", + "scope": "Runner metadata added after fixture generation; fingerprinted here, not claimed to be covered by the original fixture integrity manifest." + } + ], + "native_generation_wall_seconds": 6.2972065838985145, + "native_generation_timing_scope": "Recorded fixture construction, stepping and rendering; excludes Jev inference and this verification.", + "recorded_mujoco_version": "3.15.0", + "verification_host": { + "system": "Darwin", + "release": "25.5.0", + "machine": "arm64", + "processor": "arm", + "logical_cpu_count": 10, + "python_version": "3.12.9", + "scope": "Host running this verifier; CPU model, RAM and GPU specifications are not inferred." + }, + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "case_count": 32, + "category_counts": { + "complete": 4, + "missing_target": 4, + "moving_alias": 4, + "occluded": 4, + "unreleased": 4, + "wrong_object": 4, + "wrong_orientation": 4, + "wrong_position": 4 + }, + "physical_complete": 8, + "physical_incomplete": 24, + "zero_solver_warnings": true, + "no_added_object_attachment_constraints": true, + "no_recorded_external_forces": true, + "native_equalities_per_model": 1, + "equality_scope": "Recorded upstream gripper coupling count; verified against frozen state metadata, without recompiling models.", + "all_physical_labels_recomputed": true, + "all_telemetry_extrema_recomputed": true, + "unreleased_rgb_unknown_cases": 4, + "aliases": [ + { + "case_id": "layout-01-moving_alias", + "pixel_identical_to": "layout-01-complete", + "png_sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "png_bytes": 79345, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-02-moving_alias", + "pixel_identical_to": "layout-02-complete", + "png_sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "png_bytes": 79868, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-03-moving_alias", + "pixel_identical_to": "layout-03-complete", + "png_sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "png_bytes": 78462, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-04-moving_alias", + "pixel_identical_to": "layout-04-complete", + "png_sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "png_bytes": 77762, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + } + ], + "cases": [ + { + "id": "layout-01-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-01-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "table_support_force_n": 0.2942999990014349, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "table_support_force_n": 0.2995624074490855, + "robot_contact_force_n": 0.7619265340093823, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.3624151236609657, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-02-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-02-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "table_support_force_n": 0.3014209771616913, + "robot_contact_force_n": 0.7693653082066636, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.36239906646539655, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-03-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-03-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "table_support_force_n": 0.29429999900143444, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "table_support_force_n": 0.2639875277923961, + "robot_contact_force_n": 0.7863052928276089, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.362350885628384, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-04-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805211389336e-07, + 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Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "layouts": [ + { + "id": "layout-01", + "xy_m": [ + 0.4, + -0.1 + ], + "yaw_rad": 0.0, + "azimuth_deg": 130.0 + }, + { + "id": "layout-02", + "xy_m": [ + 0.43, + -0.16 + ], + "yaw_rad": 0.08, + "azimuth_deg": 145.0 + }, + { + "id": "layout-03", + "xy_m": [ + 0.38, + -0.07 + ], + "yaw_rad": -0.08, + "azimuth_deg": 115.0 + }, + { + "id": "layout-04", + "xy_m": [ + 0.42, + -0.13 + ], + "yaw_rad": 0.15, + "azimuth_deg": 155.0 + } + ], + "categories": [ + "complete", + "missing_target", + "wrong_object", + "wrong_position", + "wrong_orientation", + "unreleased", + "moving_alias", + "occluded" + ], + "case_ids": [ + "layout-01-complete", + "layout-01-missing_target", + "layout-01-wrong_object", + "layout-01-wrong_position", + "layout-01-wrong_orientation", + "layout-01-unreleased", + "layout-01-moving_alias", + "layout-01-occluded", + "layout-02-complete", + "layout-02-missing_target", + "layout-02-wrong_object", + "layout-02-wrong_position", + "layout-02-wrong_orientation", + "layout-02-unreleased", + "layout-02-moving_alias", + "layout-02-occluded", + "layout-03-complete", + "layout-03-missing_target", + "layout-03-wrong_object", + "layout-03-wrong_position", + "layout-03-wrong_orientation", + "layout-03-unreleased", + "layout-03-moving_alias", + "layout-03-occluded", + "layout-04-complete", + "layout-04-missing_target", + "layout-04-wrong_object", + "layout-04-wrong_position", + "layout-04-wrong_orientation", + "layout-04-unreleased", + "layout-04-moving_alias", + "layout-04-occluded" + ], + "image_size": [ + 640, + 480 + ], + "physics_timestep_s": 0.002, + "settling_s": 0.6, + "native_observation_window_s": 0.2, + "unreleased_controller": { + "stages": [ + "approach above heel", + "descend", + "close" + ], + "stage_duration_s": [ + 3.0, + 2.0, + 1.2 + ], + "heel_offset_m": -0.085, + "tcp_height_m": [ + 0.2, + 0.055, + 0.055 + ], + "jaw_command_m": [ + 0.035, + 0.035, + 0.0 + ], + "native_dt_s": 0.002, + "control_dt_s": 0.02, + "scope": "Authored approach then native finger contact; not a lift or learned manipulation." + }, + "moving_alias_velocity_m_s": [ + 0.12, + 0.0, + 0.0 + ], + "synthetic_shoe": { + "source": "packaged trainer-left collision primitives", + "width_scale": 0.5, + "mass_kg": 0.03, + "visual_colors": "Green upper for target, orange upper for decoy, cream sole and toe.", + "reason": "Declared narrow rigid proxy permits unchanged PiPER finger contact; not a full-size measured shoe." + }, + "scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "dwell_scope": "Every 2 ms native sample satisfies all thresholds for 100 consecutive ticks; no continuous-time or hardware certification claim.", + "reset_scope": "Each fixture starts from an authored state. The moving alias changes native velocity at the snapshot without advancing time; this is a declared state intervention, not a measured physical impulse.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator.", + "rgb_observability": "Single RGB cannot prove velocity, support force, release force or 200 ms dwell. Complete, moving, occluded and unreleased states require unknown. Even visibly closed fingers do not establish the 0.01 N contact-force threshold. Missing targets, wrong objects and visible pose violations permit incomplete.", + "freeze": "Generate and hash this protocol before any model requests; never choose cases or thresholds from model results." +} diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 new file mode 100644 index 0000000..a4177f7 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 @@ -0,0 +1 @@ +d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4 diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json new file mode 100644 index 0000000..cf578ea --- /dev/null +++ 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+ "errors": 0, + "not_run": 32, + "completion_accuracy": { + "correct": 0, + "answered": 0, + "planned": 32 + }, + "completion_abstentions": 0, + "observability_aware_accuracy": { + "correct": 0, + "planned": 32 + }, + "known_verdict_accuracy": { + "correct": 0, + "planned": 32 + }, + "ambiguous_abstention": { + "correct": 0, + "planned": 0 + }, + "false_completion": { + "count": 0, + "answered_incomplete": 0, + "planned_incomplete": 24, + "valid_incomplete": 0 + }, + "false_presence": { + "count": 0, + "answered_absent": 0, + "planned_absent": 8, + "valid_absent": 0 + }, + "known_usage_estimated_input_cost_usd": null, + "unknown_cost_attempts": 0, + "all_attempt_latency_ms": { + "n": 0, + "p50": null, + "p95": null, + "p99": null, + "max": null + }, + "successful_response_latency_ms": { + "n": 0, + "p50": null, + "p95": null, + "p99": null, + "max": null + } + } + ], + "scope": "Synthetic simulation fixtures; passive image-goal judgments; no robot control or trained weights." +} diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json new file mode 100644 index 0000000..f638db4 --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json @@ -0,0 +1,8000 @@ +{ + "active_trial": null, + "environment": { + "platform": "macOS-26.5.2-arm64-arm-64bit", + "python": "3.12.9" + }, + "evaluation_plan_sha256": "5f02006190d97bdaca43f139f5bf221cece23bba868a5e9ee0f424618ea87d8f", + "limitations": [ + "Authored diagnostic fixtures; four scene layouts are correlated clusters, not 32 independent real-world tasks.", + "Preloaded image bytes + request construction + image upload + hosted response + parsing; excludes fixture file loading, prerecorded rendering, live camera, actuation and physical task duration.", + "Telemetry is privileged simulator state, not validated real sensor readings.", + "Jev is a hosted experimental image evaluator; no robot learning or local Jev inference is demonstrated." + ], + "metrics": null, + "model": "jev-1.14.0", + "planned_conditions": [ + "rgb", + "rgb_telemetry" + ], + "publication_sanitization": { + "credentials_exported": false, + "omitted_fields": [ + "results[].response" + ], + "raw_provider_response_exported": false + }, + "results": [ + { + "attempts": 0, + "case_id": "layout-02-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81061, + "format": "png", + "height": 480, + "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.24558301083743572, + "total": 0.2466249279677868 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627749443387563e-09, + "linear_speed_m_s": 9.86007596264554e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361806336e-06, + "min": 2.7627749443387563e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091117024e-06, + "min": 9.86007596264554e-10 + }, + "position_error_m": { + "max": 0.1699999858103543, + "min": 0.1699999858102173 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143505, + "min": 0.2942989247886612 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.1699999858102173, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143505, + "target_position_m": [ + 0.5999999858101832, + -0.16000010763675523, + -0.00010773874568926928 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 165351, + "request_sha256": "4b2d21390d91a0e27ed7498506c325fb33f997108f0cd688646df0c8767b06b5", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.038737+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.24558301083743572, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78325, + "format": "png", + "height": 480, + "sha256": "66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16983295790851116, + "total": 0.17058290541172028 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161676, + "request_sha256": "cf2c595e98a0953986643451655b82b56ec9b6797f0385f172992a19a09e2902", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.040171+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16983295790851116, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1689591445028782, + "total": 0.16945903189480305 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162173, + "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.041499+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1689591445028782, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 69903, + "format": "png", + "height": 480, + "sha256": "0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1554158516228199, + "total": 0.15583285130560398 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151848, + "request_sha256": "6dc62c2ce15e117efcf3c31ebf6559225eec602a00771ca335893539e0ce7b17", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.042872+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1554158516228199, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16854191198945045, + "total": 0.16895798034965992 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.362350885628384, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161164, + "request_sha256": "562192137127c05e4d4f824bf7f12fd6f1885900aad2a486b8b2026602dc2e0b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.044259+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16854191198945045, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1835001166909933, + "total": 0.18404098227620125 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336032993e-06, + "min": 2.762774894432646e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090833765e-06, + "min": 9.860076492373484e-10 + }, + "position_error_m": { + "max": 1.0856805227409043e-07, + "min": 1.0856741726656525e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.2942989247886608 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161351, + "request_sha256": "9cdeacbb6a7bfb89a52b1440577dc93a86581c29ac4a50445c64fbb9436a38cc", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.045844+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1835001166909933, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72934, + "format": "png", + "height": 480, + "sha256": "d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15729200094938278, + "total": 0.15762494876980782 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155509, + "request_sha256": "a45d75d5ee6d6d2f3f98afba864906ff46b15fdececc6dfee1f03dffe06c816c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.047550+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15729200094938278, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 85815, + "format": "png", + "height": 480, + "sha256": "9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18979120068252087, + "total": 0.1901660580188036 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 0.0009941703968526277, + "linear_speed_m_s": 0.00014160096890228054, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.0017186694278268843, + "min": 0.0009941703968526277 + }, + "linear_speed_m_s": { + "max": 0.00014160096890228054, + "min": 0.0001367889163867079 + }, + "position_error_m": { + "max": 0.00112237494618688, + "min": 0.0010952390133150847 + }, + "robot_contact_force_n": { + "max": 0.7693653082066636, + "min": 0.7684190832360072 + }, + "table_support_force_n": { + "max": 0.3046279922938624, + "min": 0.3014209771616913 + }, + "yaw_error_rad": { + "max": 0.00569434757841164, + "min": 0.005609244269984187 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.00112237494618688, + "robot_contact_force_n": 0.7693653082066636, + "table_support_force_n": 0.3014209771616913, + "target_position_m": [ + 0.43040182038492686, + -0.1610479818214479, + -0.00010842273593493796 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.00569434757841164 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 170145, + "request_sha256": "ba1ab4131973a5d3833b50c1af81ec168dded4ea50209a2b9e88b6d64c07849a", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.049350+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18979120068252087, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14620809815824032, + "total": 0.14654104597866535 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159007, + "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.051252+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14620809815824032, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79569, + "format": "png", + "height": 480, + "sha256": "66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18862495198845863, + "total": 0.18920795992016792 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.7627747152164955e-09, + "linear_speed_m_s": 9.86007598072673e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233360100356e-06, + "min": 2.7627747152164955e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090957944e-06, + "min": 9.86007598072673e-10 + }, + "position_error_m": { + "max": 1.0856805243889215e-07, + "min": 1.0856741730644736e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143444, + "min": 0.29429892478866126 + }, + "yaw_error_rad": { + "max": 1.570796324598559, + "min": 1.570796324598342 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243889215e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143444, + "target_position_m": [ + 0.3800001040012452, + -0.07000003115707004, + -0.00010773874568926854 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983425 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162452, + "request_sha256": "2ea701cfba448a105267bbb27f8303456ca072d2d07a4a930d21034853fb262d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.053912+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18862495198845863, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 86362, + "format": "png", + "height": 480, + "sha256": "8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1669169869273901, + "total": 0.16720802523195744 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 0.002606933470747755, + "linear_speed_m_s": 0.00010064949189093267, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.004742951667749043, + "min": 0.002606933470747755 + }, + "linear_speed_m_s": { + "max": 0.00010064949189093267, + "min": 9.490361836146501e-05 + }, + "position_error_m": { + "max": 0.0010491825564488413, + "min": 0.0010350014788722895 + }, + "robot_contact_force_n": { + "max": 0.790406083741399, + "min": 0.7881504132862833 + }, + "table_support_force_n": { + "max": 0.3148922546022384, + "min": 0.3142693700788355 + }, + "yaw_error_rad": { + "max": 0.03691679299236298, + "min": 0.03690074331083231 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.0010491825564488413, + "robot_contact_force_n": 0.790406083741399, + "table_support_force_n": 0.3144476609893772, + "target_position_m": [ + 0.42104823526500446, + -0.13004457427461588, + -0.00011458521544506569 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.03690074331083231 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 168585, + "request_sha256": "6517dc9913124d034b3a54349b77f81feb05e049b0660a75c9b93d1443d0c425", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.056020+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1669169869273901, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 69903, + "format": "png", + "height": 480, + "sha256": "0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16333302482962608, + "total": 0.16379100270569324 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336032993e-06, + "min": 2.762774894432646e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090833765e-06, + "min": 9.860076492373484e-10 + }, + "position_error_m": { + "max": 1.0856805227409043e-07, + "min": 1.0856741726656525e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.2942989247886608 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 152792, + "request_sha256": "0b9a08881ff8452447d301dc7e4379139afd0cda5b98a0ad7699e03a6e433c53", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.058197+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16333302482962608, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 64921, + "format": "png", + "height": 480, + "sha256": "3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14112493954598904, + "total": 0.1415829174220562 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 148272, + "request_sha256": "1d6c918bd75bbb3d3544876daee9b4f4439c27f76d4a97126c1734d831add576", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.060536+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14112493954598904, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 87865, + "format": "png", + "height": 480, + "sha256": "2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.154959037899971, + "total": 0.1553341280668974 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 169810, + "request_sha256": "803f461156bfe7fc05d5fbbdab899f45c52b561e1700f4c8ba68873e3635d6dd", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.062950+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.154959037899971, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 71367, + "format": "png", + "height": 480, + "sha256": "dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15566707588732243, + "total": 0.15600002370774746 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155348, + "request_sha256": "6e3c7bda63e2584be56d9f5ffbcdf6228ba3f44c1d45e4d53f708f7e873d12da", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.065408+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15566707588732243, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79111, + "format": "png", + "height": 480, + "sha256": "56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16829092055559158, + "total": 0.1686250325292349 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627746996178486e-09, + "linear_speed_m_s": 9.860076309132707e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233360765137e-06, + "min": 2.7627746996178486e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509120737e-06, + "min": 9.860076309132707e-10 + }, + "position_error_m": { + "max": 1.0856805256849158e-07, + "min": 1.0856741754859924e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014349, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 1.5707963245985561, + "min": 1.57079632459834 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805256849158e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014349, + "target_position_m": [ + 0.40000010615852655, + -0.10000002274619275, + -0.00010773874568926942 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983405 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162876, + "request_sha256": "5427dbcbe70b437b4949c2ef974905d4710ea0c994e8d92fd207d1017d494d6d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.068035+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16829092055559158, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 86362, + "format": "png", + "height": 480, + "sha256": "8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16229203902184963, + "total": 0.16262498684227467 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167607, + "request_sha256": "053c7b9d62bc72c6fccfee44a071cc0cb5c9fc4a09cef15f568ae958532d7e8e", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.070736+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16229203902184963, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 84090, + "format": "png", + "height": 480, + "sha256": "3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15604193322360516, + "total": 0.15637511387467384 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 166918, + "request_sha256": "29650676bc836d7065cc13f79ef3e1dbdadc7a139bfa45564dfd337c24f4918c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.073495+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15604193322360516, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 62348, + "format": "png", + "height": 480, + "sha256": "29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15787500888109207, + "total": 0.1581660471856594 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336175752e-06, + "min": 2.7627745907984584e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509081208e-06, + "min": 9.860076016164131e-10 + }, + "position_error_m": { + "max": 1.0856805211389336e-07, + "min": 1.085674170840469e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 144539, + "request_sha256": "ec0eb371382c7fd7b35408483f2c9978fce56c17d2f6355bb4027ed68d4977af", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.076313+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15787500888109207, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 62348, + "format": "png", + "height": 480, + "sha256": "29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.151375075802207, + "total": 0.15174993313848972 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 143593, + "request_sha256": "644de408fef8e6cb532ef8b5144f8d87742837887e4f05984b55341187a23a5b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.079470+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.151375075802207, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1804581843316555, + "total": 0.18079206347465515 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336044342e-06, + "min": 2.7627744970784057e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091606234e-06, + "min": 9.860075820885874e-10 + }, + "position_error_m": { + "max": 1.0856805225155038e-07, + "min": 1.0856741727623932e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.2942989247886606 + }, + "yaw_error_rad": { + "max": 2.1965561156353097e-09, + "min": 2.196340269667258e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163122, + "request_sha256": "22c765474ea8183e3002b7f92eb27d13136b75d5e1515a007133afc05d459ec9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.082541+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1804581843316555, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 71367, + "format": "png", + "height": 480, + "sha256": "dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15029101632535458, + "total": 0.15058298595249653 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 154718, + "request_sha256": "f73d1ad36b4b6537aec4ac4c603a04db290f1a02263ed7b48f65de7f8641edc9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.085770+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15029101632535458, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80112, + "format": "png", + "height": 480, + "sha256": "441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1573751214891672, + "total": 0.15762518160045147 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163463, + "request_sha256": "14d0e74adc997a0af6a29feb0bb8b5c7180fe519679936fa90265f896a87a350", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.089228+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1573751214891672, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1619590912014246, + "total": 0.16233418136835098 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163219, + "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.092502+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1619590912014246, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80061, + "format": "png", + "height": 480, + "sha256": "83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17083296552300453, + "total": 0.17112493515014648 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163519, + "request_sha256": "b9b3db8ea54fbadede8971e74c99fd7fecb513696e6a57c86f43b8a392b15cee", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.096129+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17083296552300453, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81061, + "format": "png", + "height": 480, + "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16583316028118134, + "total": 0.1662911381572485 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164412, + "request_sha256": "0caffd3a26a3698840489fb4f7d7628a99d28dd969ab7e968b681dbf57380666", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.099757+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16583316028118134, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 87865, + "format": "png", + "height": 480, + "sha256": "2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17449981532990932, + "total": 0.1748749054968357 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 0.003368756823804283, + "linear_speed_m_s": 0.00010986191904928065, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.0040346175968384084, + "min": 0.0024920357101732454 + }, + "linear_speed_m_s": { + "max": 0.00014294490050929373, + "min": 0.00010986191904928065 + }, + "position_error_m": { + "max": 0.0006551670719148867, + "min": 0.0006474789378581962 + }, + "robot_contact_force_n": { + "max": 0.7878949274134518, + "min": 0.7857892493442301 + }, + "table_support_force_n": { + "max": 0.2640607875145524, + "min": 0.25922471284709236 + }, + "yaw_error_rad": { + "max": 0.003720938606997237, + "min": 0.003703847249796388 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.0006551670719148867, + "robot_contact_force_n": 0.7863052928276089, + "table_support_force_n": 0.2639875277923961, + "target_position_m": [ + 0.38020236953882225, + -0.0693768704293019, + -9.617134314363833e-05 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.003703847249796388 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 170795, + "request_sha256": "8e094213a08d6ed860d1f868517b5a22417bb89f93029779f41f2d5974f02ad2", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.103393+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17449981532990932, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15645800158381462, + "total": 0.15670806169509888 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.3623192350084462, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159768, + "request_sha256": "626df301847db14cad7060405b1875996cef9c3f6802e820440fc5cc3f47ac22", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.107079+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15645800158381462, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72825, + "format": "png", + "height": 480, + "sha256": "b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16075000166893005, + "total": 0.16116700135171413 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155653, + "request_sha256": "6680c0e5be92f50394933cd91f7fb856cedfc428d98983f245ba5719c920bb37", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.111048+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16075000166893005, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 85815, + "format": "png", + "height": 480, + "sha256": "9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15600002370774746, + "total": 0.15624985098838806 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 169166, + "request_sha256": "f6c0b2aa823c13f89f27aabcbb83816816e0f68cf026923a8d8916c170287a83", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.115004+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15600002370774746, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16045919619500637, + "total": 0.16095908358693123 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162173, + "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.119090+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16045919619500637, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 64921, + "format": "png", + "height": 480, + "sha256": "3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1646250020712614, + "total": 0.1650000922381878 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361619074e-06, + "min": 2.7627747344185066e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091174393e-06, + "min": 9.86007602226177e-10 + }, + "position_error_m": { + "max": 1.0856805243386722e-07, + "min": 1.0856741745389504e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866115 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 149220, + "request_sha256": "f58a5417aaa6d0d5528b2c0c672e2fd6dfd9fb13080c382c080475529b938ca9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.123177+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1646250020712614, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 83583, + "format": "png", + "height": 480, + "sha256": "37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1566251739859581, + "total": 0.15700003132224083 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 166411, + "request_sha256": "fbdf1c3a54e1d8f3a51b5542528e4f621b5dd94e93db2228bc21828fbf8e4d85", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.127455+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1566251739859581, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81396, + "format": "png", + "height": 480, + "sha256": "266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15649991109967232, + "total": 0.15691597945988178 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163341, + "request_sha256": "3908d00ae374e96f852d1e026bfd79ba6c1b2457611d9ae362168ae2ab1fd5b1", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.131662+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15649991109967232, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1574999187141657, + "total": 0.15787500888109207 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 160407, + "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.136135+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1574999187141657, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16158400103449821, + "total": 0.1618748065084219 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.3624151236609657, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162934, + "request_sha256": "0b5063721c28b72b90cd69cf3ff5eccd3d6a9b4e1f4b6e04d4acdeba9c5813e1", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.140603+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16158400103449821, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78104, + "format": "png", + "height": 480, + "sha256": "3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14870893210172653, + "total": 0.14904187992215157 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159349, + "request_sha256": "e0143d52be87f1d72e4d955ea76e61e6fcdda047f9725766666cc6be808135ed", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.145099+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14870893210172653, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79100, + "format": "png", + "height": 480, + "sha256": "78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16175000928342342, + "total": 0.1620408147573471 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161675, + "request_sha256": "c48a75ef61b1c37da9f78fced2ad8c8fb64bb824764df2bafb810b5272ddb9b0", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.149715+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16175000928342342, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15062489546835423, + "total": 0.15095784328877926 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159007, + "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.154368+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15062489546835423, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80849, + "format": "png", + "height": 480, + "sha256": "a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14754105359315872, + "total": 0.14783302322030067 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162094, + "request_sha256": "691dbe273c38c698069ed53d87cc9b4bce9c714f66fd76e11d89569a7be6de69", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.159028+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14754105359315872, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79569, + "format": "png", + "height": 480, + "sha256": "66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1476658508181572, + "total": 0.14787493273615837 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161514, + "request_sha256": "fd64d5be5c9fefcc4cbb3c3b42e73bafb28bf0fe169af7cb0dc2b64ec7b0bc32", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.163763+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1476658508181572, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79111, + "format": "png", + "height": 480, + "sha256": "56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15441584400832653, + "total": 0.15474995598196983 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161939, + "request_sha256": "9aad7d221c1a2815360c5a7c7fc5d71ed186731fe9d727724868bedb5eb2042c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.168781+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15441584400832653, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 68346, + "format": "png", + "height": 480, + "sha256": "747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1557080540806055, + "total": 0.15600002370774746 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336044342e-06, + "min": 2.7627744970784057e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091606234e-06, + "min": 9.860075820885874e-10 + }, + "position_error_m": { + "max": 1.0856805225155038e-07, + "min": 1.0856741727623932e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.2942989247886606 + }, + "yaw_error_rad": { + "max": 2.1965561156353097e-09, + "min": 2.196340269667258e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 152123, + "request_sha256": "207e6f5519077f996117540549c91f9b242b6811633219a3d98f3141966939c6", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.173751+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1557080540806055, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 70111, + "format": "png", + "height": 480, + "sha256": "9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.158791895955801, + "total": 0.15908293426036835 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151986, + "request_sha256": "38c739b8b55cb6da3677e531b81e3868e16ced493b42c708c8e027fb8a653b71", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.178809+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.158791895955801, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 76269, + "format": "png", + "height": 480, + "sha256": "a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16958406195044518, + "total": 0.16979197971522808 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627747672417554e-09, + "linear_speed_m_s": 9.86007598437561e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361383416e-06, + "min": 2.7627747672417554e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091079773e-06, + "min": 9.86007598437561e-10 + }, + "position_error_m": { + "max": 1.0856805194707237e-07, + "min": 1.0856741703541562e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 1.5707963245985561, + "min": 1.5707963245983396 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805194707237e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4200001083656286, + -0.13000000662664574, + -0.0001077387456892685 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.57079632459834 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 158446, + "request_sha256": "cc06c03336fd573f3f93300c40f09356b1e6e25ce2e0e7f4c6f6c2b7e5b9e0eb", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.184334+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16958406195044518, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 76269, + "format": "png", + "height": 480, + "sha256": "a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15383283607661724, + "total": 0.1541248057037592 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 157514, + "request_sha256": "c7abe212b11813b00ca8d4dcea622a21d1aff59d03c4a29bb21feb10939d813b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.189846+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15383283607661724, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 84090, + "format": "png", + "height": 480, + "sha256": "3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.20058383233845234, + "total": 0.20095892250537872 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 0.003462491733548149, + "linear_speed_m_s": 9.969011014506973e-05, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.005988177262940627, + "min": 0.003462491733548149 + }, + "linear_speed_m_s": { + "max": 9.969011014506973e-05, + "min": 9.643102940812748e-05 + }, + "position_error_m": { + "max": 0.005753664112566862, + "min": 0.00574563428980446 + }, + "robot_contact_force_n": { + "max": 0.7619265340093823, + "min": 0.760598912244461 + }, + "table_support_force_n": { + "max": 0.30103792478353864, + "min": 0.2995624074490855 + }, + "yaw_error_rad": { + "max": 0.08440615838439973, + "min": 0.08437097640564 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.00574563428980446, + "robot_contact_force_n": 0.7619265340093823, + "table_support_force_n": 0.2995624074490855, + "target_position_m": [ + 0.40145485082066407, + -0.09444160792298799, + -0.00010962509298383693 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.08440615838439973 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167888, + "request_sha256": "27fd66094c3efe56842da714acc7be74fc97e09dea4ad67d7a4fe29ffa17d7a7", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.195262+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.20058383233845234, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79100, + "format": "png", + "height": 480, + "sha256": "78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.19983411766588688, + "total": 0.20112493075430393 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161045, + "request_sha256": "3650b3cd6be734b696e8157da729427b1856d68eb905bce765cff3556c099814", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.201022+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.19983411766588688, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1655002124607563, + "total": 0.16587506979703903 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163219, + "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.206717+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1655002124607563, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.2026660367846489, + "total": 0.20316592417657375 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361619074e-06, + "min": 2.7627747344185066e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091174393e-06, + "min": 9.86007602226177e-10 + }, + "position_error_m": { + "max": 1.0856805243386722e-07, + "min": 1.0856741745389504e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866115 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164167, + "request_sha256": "faa58fbc2678ab387f1f7f8b7c24aa781e0259cbf19a89a7459932ecb2a5d768", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.212481+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.2026660367846489, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81396, + "format": "png", + "height": 480, + "sha256": "266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1894170418381691, + "total": 0.18974998965859413 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774718512132e-09, + "linear_speed_m_s": 9.86007647555542e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361579335e-06, + "min": 2.762774718512132e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090868942e-06, + "min": 9.86007647555542e-10 + }, + "position_error_m": { + "max": 0.1699999688431964, + "min": 0.16999996884296226 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.16999996884296226, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.5499999688429305, + -0.07000010400124504, + -0.00010773874568926812 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164275, + "request_sha256": "8a5b29305dec3510c2c7960557a0ef043eafb4237293f6b1507f3197242607fa", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.218753+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1894170418381691, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72825, + "format": "png", + "height": 480, + "sha256": "b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1798751763999462, + "total": 0.1805000938475132 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 156283, + "request_sha256": "150e2666d6e63b662012e973a5ab1e6e614d3492b7f324cdf3e9de9a3f0f4c98", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.224752+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1798751763999462, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 68346, + "format": "png", + "height": 480, + "sha256": "747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1576668582856655, + "total": 0.15799980610609055 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151174, + "request_sha256": "f653413e688093b5eec5d7b0597618d5946991f0921fb372650c7f562decfe95", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.230981+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1576668582856655, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78104, + "format": "png", + "height": 480, + "sha256": "3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.19812490791082382, + "total": 0.19870884716510773 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159979, + "request_sha256": "b364766ccfff769c100afd83646101a3b4660191294982804942390f264d1cc3", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.237321+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.19812490791082382, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 83583, + "format": "png", + "height": 480, + "sha256": "37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.2163329627364874, + "total": 0.21679187193512917 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627747009029746e-09, + "linear_speed_m_s": 9.860075879684076e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233362534212e-06, + "min": 2.7627747009029746e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509173234e-06, + "min": 9.860075879684076e-10 + }, + "position_error_m": { + "max": 0.1699999772540272, + "min": 0.1699999772538407 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143505, + "min": 0.2942989247886605 + }, + "yaw_error_rad": { + "max": 2.1965561161782934e-09, + "min": 2.1963402702132865e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.1699999772538407, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143505, + "target_position_m": [ + 0.5699999772538076, + -0.10000010615852632, + -0.00010773874568926917 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561161782934e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167351, + "request_sha256": "a0ed0c8c2f72b936e34711c88f874179a2af286a17e8eaf9068dfe41bdf8e778", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.243762+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.2163329627364874, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 70111, + "format": "png", + "height": 480, + "sha256": "9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1909579150378704, + "total": 0.19158306531608105 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151356, + "request_sha256": "517d5035ec97f26d3d2c5ba307c827dac3f5e5a2cc831b82d648c94cdb6f2ef8", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.250598+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1909579150378704, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72934, + "format": "png", + "height": 480, + "sha256": "d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17470913007855415, + "total": 0.1752499956637621 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 154879, + "request_sha256": "4a5cfb98445b511272f4eece16db8e09eedb075da0ce456fedc68d1280dda3f6", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.257469+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17470913007855415, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1829999964684248, + "total": 0.18366705626249313 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 160407, + "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.264310+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1829999964684248, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18179207108914852, + "total": 0.18225004896521568 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.36239906646539655, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163981, + "request_sha256": "2b2cf534f89561d955526b1bb1db41baba75a591546dcd80349293a345ae7877", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.271046+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18179207108914852, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80849, + "format": "png", + "height": 480, + "sha256": "a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18854206427931786, + "total": 0.18925010226666927 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.762774742684285e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361281146e-06, + "min": 2.762774742684285e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090883427e-06, + "min": 9.86007605782391e-10 + }, + "position_error_m": { + "max": 0.16999999337348212, + "min": 0.16999999337338886 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143494, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.16999999337338886, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143494, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163033, + "request_sha256": "9a86c3e586b664e74763f17964bb1f41aae3dc2d3e938a2af44de193e52754d8", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.277869+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18854206427931786, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80061, + "format": "png", + "height": 480, + "sha256": "83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16075000166893005, + "total": 0.1612498890608549 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162889, + "request_sha256": "57238b47f9786cb04bcc447130e5b4be6db4fe108092005392ea161d36e7b8f9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.284897+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16075000166893005, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17070886678993702, + "total": 0.17099990509450436 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336175752e-06, + "min": 2.7627745907984584e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509081208e-06, + "min": 9.860076016164131e-10 + }, + "position_error_m": { + "max": 1.0856805211389336e-07, + "min": 1.085674170840469e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159953, + "request_sha256": "63d8307d311a3bfd90ef515017c251ee82d3e4b714f7fd7bd716bf53b4d5974d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.291842+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17070886678993702, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80112, + "format": "png", + "height": 480, + "sha256": "441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17170794308185577, + "total": 0.17200014553964138 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164093, + "request_sha256": "01b8450027128d7fe455f7d3b39c54fcac74a8fe25ff196aa0e15237b9f4f35f", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.298993+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17170794308185577, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78325, + "format": "png", + "height": 480, + "sha256": "66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1871250569820404, + "total": 0.18762494437396526 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.762774938435085e-09, + "linear_speed_m_s": 9.86007540976695e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.04682333632664e-06, + "min": 2.762774938435085e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091088337e-06, + "min": 9.86007540976695e-10 + }, + "position_error_m": { + "max": 1.0856805246501234e-07, + "min": 1.085674174082394e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.29429892478866126 + }, + "yaw_error_rad": { + "max": 1.5707963245985526, + "min": 1.5707963245983363 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805246501234e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.4300001076367553, + -0.16000001418981774, + -0.0001077387456892687 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983367 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162608, + "request_sha256": "b525b55a98301c6e1360cf7f82ec7a4215658fd7ba841fa75de19aa3ceea649e", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.306196+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1871250569820404, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + } + ], + "schema": "dvidia.image-goal-results/v1", + "status": "not_run", + "updated_at": "2026-10-19T03:32:08.310051+00:00" +} diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json new file mode 100644 index 0000000..7eff49c --- /dev/null +++ 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{ + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258797970918 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129091145462 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444008992898 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562825, + -0.00010773876960802349 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965562979620046e-09, + "linear_speed_m_s": 1.8592110975236108e-09, + "angular_speed_rad_s": 5.082557032305943e-09, + "table_support_force_n": 0.2942999981164978, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167902921949 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258800831396 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129095050487 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444012845948 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562834, + -0.00010773876614262785 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.196556353473156e-09, + "linear_speed_m_s": 1.73269781854837e-09, + "angular_speed_rad_s": 4.75000607513972e-09, + "table_support_force_n": 0.2942999982447276, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167905847472 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258803495461 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129098692335 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0742244401643749 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 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"other_geom": "workstation_table_top", + "normal_force_n": 0.06715129110965216 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444028525785 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562867, + -0.00010773875204777444 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565200066095e-09, + "linear_speed_m_s": 1.2181346350210967e-09, + "angular_speed_rad_s": 3.3857205809038858e-09, + "table_support_force_n": 0.2942999987662177, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167917723113 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0729025881431115 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0671512911353447 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444031053038 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562872, + -0.00010773874977728153 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.196556547762185e-09, + "linear_speed_m_s": 1.1352464606201532e-09, + "angular_speed_rad_s": 3.163897692552555e-09, + "table_support_force_n": 0.29429999885021246, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0800216791963203 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258816049928 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129115930556 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444033408733 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562878, + -0.00010773874766128403 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.196556547762185e-09, + "linear_speed_m_s": 1.0579987445637703e-09, + "angular_speed_rad_s": 2.9565634257360078e-09, + "table_support_force_n": 0.2942999989284881, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167921409827 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258817669346 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0671512911816513 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444035604506 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09, + "table_support_force_n": 0.29429999900143494, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167923065535 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258819177643 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129120249083 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444037651231 + } + ], + "instantaneous_conditions_met": false + } + ], + "solver_warnings": 0, + "object_attachment_constraints_added": 0, + "model_equalities": 1, + "external_forces_applied": false +} diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py new file mode 100644 index 0000000..05130db --- /dev/null +++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 +"""Independently audit frozen image-goal evidence, without rendering or an API. + +The numerical predicate below deliberately does not call the simulator's +``observe``/``_measure`` functions or trust their stored completion flags. +It checks recorded native evidence, not a fresh simulation or hardware run. +""" +from __future__ import annotations + +import argparse +from collections import Counter +from hashlib import sha256 +import json +import math +import os +from pathlib import Path, PurePosixPath +import platform +import sys + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from dvidia_training.image_goal_scenes import CRITERIA, expected_labels # noqa: E402 + +SCHEMA = "dvidia.image-goal-native-validation/v1" +METRICS = ("position_error_m", "yaw_error_rad", "linear_speed_m_s", + "angular_speed_rad_s", "table_support_force_n", "robot_contact_force_n") + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def finite(value, label): + require(type(value) in (int, float) and math.isfinite(value), f"Invalid finite number: {label}") + return float(value) + + +def same_number(actual, expected, label): + if expected is None: + require(actual is None, f"Expected null: {label}") + else: + require(math.isclose(finite(actual,label), finite(expected,label), rel_tol=1e-10, abs_tol=1e-12), + f"Numeric mismatch: {label}") + + +def regular_file(root, name): + require(isinstance(name,str) and "\\" not in name, "Manifest path must be POSIX text") + relative=PurePosixPath(name) + require(not relative.is_absolute() and relative.as_posix()==name and + all(part not in ("", ".", "..") for part in relative.parts), "Unsafe manifest path") + path=root.joinpath(*relative.parts) + require(all(not p.is_symlink() for p in (path,*path.parents) if p==root or root in p.parents), + f"Symlink in fixture path: {name}") + require(path.is_file(), f"Missing regular fixture file: {name}") + return path + + +def load_json(path): + def reject(value): + raise ValueError(f"Non-finite JSON value in {path.name}: {value}") + return json.loads(path.read_text(encoding="utf-8"),parse_constant=reject) + + +def physical_predicate(row, criteria): + """Recompute from numbers, ignoring instantaneous_conditions_met labels.""" + require(type(row["target_present"]) is bool, "Presence must be a boolean") + if not row["target_present"]: + require(all(row[key] is None for key in METRICS[:4]), "Absent target has pose/speed evidence") + return False + values={key:finite(row[key],key) for key in METRICS} + require(all(value>=0 for value in values.values()), "Negative scalar physical measurement") + return (values["position_error_m"]<=criteria["position_tolerance_m"] + and values["yaw_error_rad"]<=criteria["yaw_tolerance_rad"] + and values["linear_speed_m_s"]<=criteria["linear_speed_max_m_s"] + and values["angular_speed_rad_s"]<=criteria["angular_speed_max_rad_s"] + and values["table_support_force_n"]>criteria["support_force_min_n"] + and values["robot_contact_force_n"]<=criteria["robot_force_max_n"]) + + +def recompute_truth(state, criteria, dt): + current=state["current_measurement"] + history=state["history"] + require(isinstance(history,list) and len(history)<=100, "Unexpected native observation window") + current_ok=physical_predicate(current,criteria) + count=0 + for row in reversed(history): + if not physical_predicate(row,criteria): + break + count+=1 + dwell=count*dt if current_ok else 0. + return {"present":current["target_present"], + "complete":bool(current_ok and dwell+1e-12>=criteria["continuous_dwell_s"]), + "physical_conditions_dwell_s":dwell, + "position_error_m":current["position_error_m"], + "yaw_error_rad":current["yaw_error_rad"], + "supported":finite(current["table_support_force_n"],"support")>criteria["support_force_min_n"], + "released":finite(current["robot_contact_force_n"],"robot contact")<=criteria["robot_force_max_n"], + "linear_speed_m_s":current["linear_speed_m_s"], + "angular_speed_rad_s":current["angular_speed_rad_s"]} + + +def compare_truth(stored, derived, label): + require(set(stored)==set(derived), f"Truth fields differ: {label}") + for key,value in derived.items(): + if type(value) is bool: + require(type(stored[key]) is bool and stored[key] is value, f"Truth mismatch: {label}.{key}") + else: + same_number(stored[key],value,f"{label}.{key}") + + +def verify_snapshot(state, layout): + """Cross-check raw native coordinates, independent of measurement labels.""" + measurement=state["current_measurement"] + for name in ("qpos","qvel","ctrl"): + require(isinstance(state[name],list), f"Invalid {name}") + for value in state[name]: + finite(value,name) + require(len(state["ctrl"])==7, "Expected seven pinned PiPER actuator commands") + if not measurement["target_present"]: + return + qpos,qvel=state["qpos"],state["qvel"] + require(len(qpos)==15 and len(qvel)==14, "Expected PiPER plus one free shoe") + for actual,recorded in zip(qpos[8:11],measurement["target_position_m"]): + same_number(actual,recorded,"free-body target position") + xy_error=math.hypot(qpos[8]-layout["xy_m"][0],qpos[9]-layout["xy_m"][1]) + same_number(xy_error,measurement["position_error_m"],"XY goal error") + w,x,y,z=qpos[11:15] + same_number(sum(v*v for v in (w,x,y,z)),1.,"unit free-body quaternion") + yaw=math.atan2(2*(w*z+x*y),1-2*(y*y+z*z)) + yaw_error=abs(math.atan2(math.sin(yaw-layout["yaw_rad"]),math.cos(yaw-layout["yaw_rad"]))) + same_number(yaw_error,measurement["yaw_error_rad"],"yaw goal error") + same_number(math.sqrt(sum(v*v for v in qvel[8:11])),measurement["linear_speed_m_s"],"native linear speed") + same_number(math.sqrt(sum(v*v for v in qvel[11:14])),measurement["angular_speed_rad_s"],"native angular speed") + + +def verify_telemetry(state, inputs, dt): + current,history=state["current_measurement"],state["history"] + require("complete" not in inputs and "instantaneous_conditions_met" not in inputs, + "A verdict leaked into measured inputs") + for key in ("target_present","target_position_m",*METRICS): + require(inputs[key]==current[key], f"Telemetry snapshot differs: {key}") + require(inputs["native_observation_count"]==len(history), "Native tick count differs") + require(inputs["target_present_observation_count"]==sum(row["target_present"] for row in history), + "Presence observation count differs") + same_number(inputs["native_timestep_s"],dt,"native dt") + same_number(inputs["observation_window_s"],len(history)*dt,"observation duration") + for key in METRICS: + values=[row[key] for row in history if row[key] is not None] + expected={"min":min(values) if values else None,"max":max(values) if values else None} + for statistic,value in expected.items(): + same_number(inputs["native_window_extrema"][key][statistic],value,f"{key}.{statistic}") + + +def verify(fixtures): + root=Path(fixtures).absolute() + require(root.is_dir() and not root.is_symlink(), "Fixtures must be a regular directory") + integrity=load_json(regular_file(root,"integrity.json")) + records=integrity["files"] + names=[record["path"] for record in records] + require(len(set(names))==len(names), "Duplicate integrity path") + actual={p.relative_to(root).as_posix() for p in root.rglob("*") if p.is_file()} + required=set(names)|{"integrity.json"} + require(required<=actual and actual-required<={"evaluation-plan.json"}, + "Integrity manifest coverage differs or unknown additional files exist") + additional_metadata=[] + for name in sorted(actual-required): + data=regular_file(root,name).read_bytes() + additional_metadata.append({"path":name,"bytes":len(data),"sha256":sha256(data).hexdigest(), + "scope":"Runner metadata added after fixture generation; fingerprinted here, not claimed to be covered by the original fixture integrity manifest."}) + byte_count=0 + for record in records: + path=regular_file(root,record["path"]) + data=path.read_bytes() + require(len(data)==record["bytes"], f"Byte count differs: {record['path']}") + require(sha256(data).hexdigest()==record["sha256"], f"Hash differs: {record['path']}") + byte_count+=len(data) + protocol_path=regular_file(root,"protocol.json") + protocol=load_json(protocol_path) + manifest=load_json(regular_file(root,"cases.json")) + protocol_digest=sha256(protocol_path.read_bytes()).hexdigest() + require(protocol_digest==regular_file(root,"protocol.sha256").read_text().strip()==manifest["protocol_sha256"], + "Protocol digests disagree") + require(protocol["criteria"]==manifest["criteria"]==CRITERIA,"Criteria differ from the frozen verifier contract") + criteria=protocol["criteria"] + dt=finite(protocol["physics_timestep_s"],"physics timestep") + same_number(dt,.002,"fixed native timestep") + layouts={row["id"]:row for row in protocol["layouts"]} + cases=manifest["cases"] + require(len(cases)==32 and {case["id"] for case in cases}==set(protocol["case_ids"]), + "Frozen case coverage differs") + category_counts=Counter(case["category"] for case in cases) + require(set(category_counts)==set(protocol["categories"]) and set(category_counts.values())=={4}, + "Unbalanced category coverage") + rows=[];states={} + for case in cases: + identifier=case["id"] + state=load_json(regular_file(root,case["state_file"])) + states[identifier]=state + truth=recompute_truth(state,criteria,dt) + compare_truth(state["physical_truth"],truth,identifier+".state") + compare_truth(case["physical_truth"],truth,identifier+".case") + verify_snapshot(state,layouts[case["layout_id"]]) + verify_telemetry(state,case["measured_inputs"],dt) + require(state["solver_warnings"]==0,"Native solver warning recorded") + require(state["object_attachment_constraints_added"]==0,"Object attachment recorded") + require(state["model_equalities"]==1,"Unexpected equality constraint count") + require(state["external_forces_applied"] is False,"Undeclared external force recorded") + for row in (state["current_measurement"],*state["history"]): + require(row["instantaneous_conditions_met"] is physical_predicate(row,criteria), + f"Stored instantaneous label disagrees with numbers: {identifier}") + labels=expected_labels(case["category"],truth) + for key in labels: + require(case[key]==labels[key], f"Observability labels differ: {identifier}") + visibility=case["visibility"] + if case["category"] in ("occluded","missing_target","wrong_object"): + require(visibility["visible_target_pixels"]==0 and visibility["target_box_xyxy_normalized"] is None, + "An invisible/absent target has a visible box") + else: + require(visibility["visible_target_pixels"]>0 and visibility["target_box_xyxy_normalized"] is not None, + "Visible target is missing its native segmentation box") + if case["category"]=="unreleased": + require(truth["supported"] and not truth["released"] and not truth["complete"], + "Unreleased fixture lacks supported robot contact") + require(truth["position_error_m"]<=criteria["position_tolerance_m"] and + truth["yaw_error_rad"]<=criteria["yaw_tolerance_rad"], + "Unreleased fixture is outside the goal pose") + require(case["expected_verdict"]=={"rgb":"unknown","rgb_telemetry":"incomplete"}, + "Unreleased RGB overclaims physical contact force") + rows.append({"id":identifier,"category":case["category"],"present":truth["present"], + "complete":truth["complete"],"physical_conditions_dwell_s":truth["physical_conditions_dwell_s"], + "supported":truth["supported"],"released":truth["released"], + "position_error_m":truth["position_error_m"],"yaw_error_rad":truth["yaw_error_rad"], + "table_support_force_n":state["current_measurement"]["table_support_force_n"], + "robot_contact_force_n":state["current_measurement"]["robot_contact_force_n"], + "linear_speed_m_s":truth["linear_speed_m_s"],"angular_speed_rad_s":truth["angular_speed_rad_s"], + "native_history_samples":len(state["history"]),"expected_verdict":labels["expected_verdict"]}) + index={case["id"]:case for case in cases} + aliases=[] + require(len(manifest["aliases"])==4,"Expected four exact-pixel aliases") + require({pair["case_id"] for pair in manifest["aliases"]}== + {case["id"] for case in cases if case["category"]=="moving_alias"}, + "Alias manifest does not cover all four moving controls") + for pair in manifest["aliases"]: + moving,stationary=index[pair["case_id"]],index[pair["pixel_identical_to"]] + require(moving["category"]=="moving_alias" and stationary["category"]=="complete" and + moving["layout_id"]==stationary["layout_id"],"Alias identity differs") + a=regular_file(root,moving["current_image"]).read_bytes() + b=regular_file(root,stationary["current_image"]).read_bytes() + require(a==b,"Current-image PNG alias bytes differ") + require(states[moving["id"]]["qpos"]==states[stationary["id"]]["qpos"],"Alias native poses differ") + require(states[moving["id"]]["qvel"]!=states[stationary["id"]]["qvel"],"Alias native velocities do not differ") + require(not moving["physical_truth"]["complete"] and stationary["physical_truth"]["complete"], + "Alias physical truth does not differ") + require(len(states[moving["id"]]["history"])==0,"Post-intervention observation time was invented") + aliases.append({**pair,"png_sha256":sha256(a).hexdigest(),"png_bytes":len(a), + "byte_identical":True,"pose_identical":True,"velocity_and_completion_differ":True}) + completed=sum(row["complete"] for row in rows) + require(completed==8 and len(rows)-completed==24,"Expected 8 complete and 24 incomplete states") + return {"schema":SCHEMA,"status":"passed", + "scope":"Independent recomputation from frozen native state measurements/history and raw coordinates. No images re-rendered, no fresh physics run, no model service called and no hardware validation.", + "fixture_generation_scope":manifest["scope"],"telemetry_scope":manifest["telemetry_scope"], + "source_digests":{"protocol_sha256":protocol_digest, + "cases_sha256":sha256(regular_file(root,"cases.json").read_bytes()).hexdigest(), + "integrity_sha256":sha256(regular_file(root,"integrity.json").read_bytes()).hexdigest(), + "verifier_sha256":sha256(Path(__file__).read_bytes()).hexdigest()}, + "files_verified":len(records),"bytes_verified":byte_count, + "additional_runner_metadata":additional_metadata, + "native_generation_wall_seconds":manifest["generation_wall_seconds"], + "native_generation_timing_scope":"Recorded fixture construction, stepping and rendering; excludes Jev inference and this verification.", + "recorded_mujoco_version":manifest["mujoco_version"], + "verification_host":{"system":platform.system(),"release":platform.release(), + "machine":platform.machine(),"processor":platform.processor() or None, + "logical_cpu_count":os.cpu_count(),"python_version":platform.python_version(), + "scope":"Host running this verifier; CPU model, RAM and GPU specifications are not inferred."}, + "criteria":criteria,"case_count":len(rows),"category_counts":dict(sorted(category_counts.items())), + "physical_complete":completed,"physical_incomplete":len(rows)-completed, + "zero_solver_warnings":True,"no_added_object_attachment_constraints":True, + "no_recorded_external_forces":True,"native_equalities_per_model":1, + "equality_scope":"Recorded upstream gripper coupling count; verified against frozen state metadata, without recompiling models.", + "all_physical_labels_recomputed":True,"all_telemetry_extrema_recomputed":True, + "unreleased_rgb_unknown_cases":4,"aliases":aliases,"cases":rows} + + +def main(argv=None): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument("--fixtures",required=True,type=Path) + parser.add_argument("--output",required=True,type=Path) + args=parser.parse_args(argv) + require(args.output.absolute()!=args.fixtures.absolute() and args.fixtures.absolute() not in args.output.absolute().parents, + "Write validation outside the frozen fixture directory") + try: + result=verify(args.fixtures) + code=0 + except (ValueError,KeyError,TypeError,OSError,json.JSONDecodeError) as error: + result={"schema":SCHEMA,"status":"failed","error":str(error)} + code=1 + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(result,indent=2,allow_nan=False)+"\n",encoding="utf-8") + print(json.dumps({key:result[key] for key in ("status","case_count","physical_complete","physical_incomplete","files_verified","bytes_verified","error") if key in result})) + return code + + +if __name__=="__main__": + raise SystemExit(main()) diff --git a/docs/feed.xml b/docs/feed.xml index 2e0b9c4..0dca429 100644 --- a/docs/feed.xml +++ b/docs/feed.xml @@ -1,2 +1,2 @@ -DVIDIA Researchhttps://research.dvidia.org/Research footprints on demonstrations, calibrated simulation and robot skills.enA visual goal still needs physical feedbackhttps://research.dvidia.org/papers/goal-images-physical-feedback/https://research.dvidia.org/papers/goal-images-physical-feedback/Sun, 18 Oct 2026 12:00:00 GMT14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked. A goal image specifies a destination; physical feedback verifies whether a robot gets there. We describe a bounded decision role for TypeSafe Jev, distinguish it from action-conditioned world-model planning, and propose an independently scored reaching experiment. A separate passive audit reproduces fourteen previously inspected rigid-box arm episodes exactly and records 249,502 native ticks. Millisecond contact gaps hidden between stored observations become visible, while task outcomes remain unchanged. Complete inputs, traces, source snapshots and a verified downloader preserve every case. This is measurement work, not a new policy benchmark, PiPER shoe result or hardware qualification.Give the gripper a fair testhttps://research.dvidia.org/papers/shoe-graspability-v03/https://research.dvidia.org/papers/shoe-graspability-v03/Wed, 14 Oct 2026 12:00:00 GMT24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake. A frozen 24-attempt PiPER diagnostic separates three rigid shapes, 30 g and 300 g loads, and mounted versus absent rake conditions. All attempts are valid; eight of twelve lightweight controls retain a direct lift while every 300 g attempt fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result, with no net completion gain. A verified settled reference, dense contact traces, unchanged drive fingerprint and independent scoring expose limits in the modeled grasp and force response. Recorded replays and runnable CPU code retain every failure. No autonomous tool removal, retrieval, learned skill or physical hardware qualification is claimed.From reaching a shoe to keeping holdhttps://research.dvidia.org/papers/continuous-shoe-grip-v02/https://research.dvidia.org/papers/continuous-shoe-grip-v02/Tue, 13 Oct 2026 12:00:00 GMT20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified. A frozen twenty-attempt native MuJoCo comparison adds continuous acquisition checks and bounded lowering to the mounted-rake PiPER controller. Both policies complete the same 2/10 fresh cases: no gains or losses. Nine bilateral-contact-loss alarms follow a trace-verified native origin by 20–52 ms, median 30 ms; three acquisition failures are separate. Twelve supported lowering responses produce no additional completion. A non-finger-support gap remains unmonitored. Complete recorded trajectories, independent audit and development failures are public. Authored policies, synthetic contact and privileged observations establish no learned skill or physical robot qualification.Skill prerequisites: access, reach and recoveryhttps://research.dvidia.org/papers/skill-prerequisites-v01/https://research.dvidia.org/papers/skill-prerequisites-v01/Tue, 13 Oct 2026 12:00:00 GMT16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts. A frozen sixteen-attempt native MuJoCo study tests hinged shoe-box access and a mounted rake for an out-of-workspace shoe. Guarded access avoids two baseline lid collisions while task completion stays at 2/4; a transient withdrawal adds 0.70 simulated seconds without being necessary in its matched baseline. The mounted rake reaches an intermediate retrieval checkpoint in 3/4 cases, but all subsequent retained lifts fail and the short-tool case is deferred. All native traces and failures remain public. The browser replays recorded states; fresh physics runs locally. Privileged observations, synthetic contact and authored policies establish no learned skill or hardware qualification.Live PiPER shoe workstation v0.1https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/Tue, 13 Oct 2026 12:00:00 GMTLive browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified. A six-axis PiPER and its coupled gripper respond to user targets while MuJoCo runs locally in a browser worker. The final native run retains 20 valid attempts: 14/16 target trials pass, both high-target repeats fail, and jaw, impulse, disabled-table and 600-second hold checks pass. A ten-minute headless Chromium run reports 51.389 render calls per second and a 0.9999795 simulation/wall-time ratio on the tested Apple M5 host; simulated movement response is 61.9 ms p95 across 61 commands. Render calls do not measure GPU presentation. Native/WASM numerical and loaded-contact-count checks pass, while strict raw contact counts differ in 13/1,500 states. All fifteen development shoe-lift attempts fail. Complete development, failure and timing records remain separate. This authored rigid-shoe environment establishes no learned skill, calibrated material model or physical robot qualification.Reusable shoe asset library v0.1https://research.dvidia.org/papers/shoe-asset-library-v01/https://research.dvidia.org/papers/shoe-asset-library-v01/Mon, 12 Oct 2026 12:00:00 GMTSix shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified. Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.PiPER placement reliability v0.5https://research.dvidia.org/papers/piper-reliability-v05/https://research.dvidia.org/papers/piper-reliability-v05/Mon, 12 Oct 2026 12:00:00 GMT23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified. Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.PiPER contact recovery v0.4https://research.dvidia.org/papers/piper-contact-recovery-v04/https://research.dvidia.org/papers/piper-contact-recovery-v04/Sun, 11 Oct 2026 12:00:00 GMTAll modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified. A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.PiPER contact history v0.3https://research.dvidia.org/papers/piper-contact-history-v03/https://research.dvidia.org/papers/piper-contact-history-v03/Sun, 11 Oct 2026 12:00:00 GMTHistory 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified. A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.PiPER grip validation and recoveryhttps://research.dvidia.org/papers/piper-grip-validation-and-recovery/https://research.dvidia.org/papers/piper-grip-validation-and-recovery/Sun, 11 Oct 2026 12:00:00 GMTModerate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified. A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.Native grasp task benchmarkhttps://research.dvidia.org/papers/native-grasp-task-benchmark/https://research.dvidia.org/papers/native-grasp-task-benchmark/Sun, 11 Oct 2026 12:00:00 GMT6/6 simulated placements · controls 0/12 · real-time and hardware unqualified. A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.Hardware matched robot simulationhttps://research.dvidia.org/papers/hardware-matched-robot-simulation/https://research.dvidia.org/papers/hardware-matched-robot-simulation/Sun, 11 Oct 2026 12:00:00 GMTPinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured. A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.DVIDIA Observation Lab pilothttps://research.dvidia.org/papers/dvidia-observation-lab/https://research.dvidia.org/papers/dvidia-observation-lab/Sun, 11 Oct 2026 12:00:00 GMTLocal review release · motion and actuator source pilots · hardware unqualified. DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.Robotics research pipelinehttps://research.dvidia.org/papers/robotics-research-pipeline/https://research.dvidia.org/papers/robotics-research-pipeline/Fri, 09 Oct 2026 12:00:00 GMTArchitecture proposal · current simulation alpha · hardware unqualified. An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.Skillspace footage training pipelinehttps://research.dvidia.org/papers/skillspace-footage-training/https://research.dvidia.org/papers/skillspace-footage-training/Thu, 08 Oct 2026 12:00:00 GMTPublic CPU package · one native scene qualified · simulation-only. The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.Skill capsule acquisition and optimizationhttps://research.dvidia.org/papers/skill-capsule-acquisition/https://research.dvidia.org/papers/skill-capsule-acquisition/Wed, 07 Oct 2026 12:00:00 GMTSimulation-only · teacher/student 7/8 · no overall speedup. A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.Skillspace arm benchmarks and evolutionhttps://research.dvidia.org/papers/skillspace-arm-benchmarks/https://research.dvidia.org/papers/skillspace-arm-benchmarks/Wed, 07 Oct 2026 12:00:00 GMTMixed held-out result · 30/32 baseline, 29/32 updated. Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.Installing a Skillspace on a simulated armhttps://research.dvidia.org/papers/skillspace-arm-installation/https://research.dvidia.org/papers/skillspace-arm-installation/Wed, 07 Oct 2026 12:00:00 GMTPublic skill link to authored adapter · 6/6 scene placements. The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.Offline cable training environmenthttps://research.dvidia.org/papers/offline-cable-training-environment/https://research.dvidia.org/papers/offline-cable-training-environment/Wed, 07 Oct 2026 12:00:00 GMTCPU simulation-only · Controller search and frozen evaluation executed. The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.Fast accurate robot training simulationhttps://research.dvidia.org/papers/fast-accurate-robot-training-simulation/https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/Wed, 07 Oct 2026 12:00:00 GMT54 CPU fixture trials · GPU and physical transfer untested. A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.Grok robotics research on Xhttps://research.dvidia.org/papers/grok-robotics-research-on-x/https://research.dvidia.org/papers/grok-robotics-research-on-x/Wed, 07 Oct 2026 12:00:00 GMTPrimary sources checked · Experiments proposed. Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.Spatially grounded downloadable robot skillshttps://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/Wed, 07 Oct 2026 12:00:00 GMTSource review · DVIDIA experiments not run. A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.Clef and grounded video observationshttps://research.dvidia.org/papers/clef-and-grounded-video-observations/https://research.dvidia.org/papers/clef-and-grounded-video-observations/Tue, 06 Oct 2026 12:00:00 GMTFive licensed excerpts · Accuracy evaluation outstanding. A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.From Skillspace to robot skillhttps://research.dvidia.org/papers/from-skillspace-to-robot-skill/https://research.dvidia.org/papers/from-skillspace-to-robot-skill/Tue, 06 Oct 2026 12:00:00 GMTGraduation and robot training not run. An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.Turn physical footage into testable evidencehttps://research.dvidia.org/papers/physical-skill-research-agenda/https://research.dvidia.org/papers/physical-skill-research-agenda/Tue, 06 Oct 2026 12:00:00 GMTFour baseline protocols proposed. A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct. +DVIDIA Researchhttps://research.dvidia.org/Research footprints on demonstrations, calibrated simulation and robot skills.enImage-goal verification: native benchmark and Jev protocolhttps://research.dvidia.org/papers/image-goal-jev-v01/https://research.dvidia.org/papers/image-goal-jev-v01/Sun, 18 Oct 2026 12:00:00 GMT32 native fixtures · 4 identical-image pairs · 64 Jev trials not run. A frozen PiPER image-goal cohort crosses eight physical conditions with four layouts. Four moving/settled pairs have byte-identical images and different completion labels, exposing when one image cannot establish task completion. Native measurements verify 32 authored fixtures; a paired 64-request Jev protocol distinguishes RGB judgments from privileged simulator telemetry. The provider evaluation has not run because credentials were unavailable: accuracy, false-completion rates and service latency remain unmeasured. Every native state, image, frozen request hash and explicit unrun result is retained. No learned policy or hardware qualification is claimed.A visual goal still needs physical feedbackhttps://research.dvidia.org/papers/goal-images-physical-feedback/https://research.dvidia.org/papers/goal-images-physical-feedback/Sun, 18 Oct 2026 12:00:00 GMT14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked. A goal image specifies a destination; physical feedback verifies whether a robot gets there. We describe a bounded decision role for TypeSafe Jev, distinguish it from action-conditioned world-model planning, and propose an independently scored reaching experiment. A separate passive audit reproduces fourteen previously inspected rigid-box arm episodes exactly and records 249,502 native ticks. Millisecond contact gaps hidden between stored observations become visible, while task outcomes remain unchanged. Complete inputs, traces, source snapshots and a verified downloader preserve every case. This is measurement work, not a new policy benchmark, PiPER shoe result or hardware qualification.Give the gripper a fair testhttps://research.dvidia.org/papers/shoe-graspability-v03/https://research.dvidia.org/papers/shoe-graspability-v03/Wed, 14 Oct 2026 12:00:00 GMT24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake. A frozen 24-attempt PiPER diagnostic separates three rigid shapes, 30 g and 300 g loads, and mounted versus absent rake conditions. All attempts are valid; eight of twelve lightweight controls retain a direct lift while every 300 g attempt fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result, with no net completion gain. A verified settled reference, dense contact traces, unchanged drive fingerprint and independent scoring expose limits in the modeled grasp and force response. Recorded replays and runnable CPU code retain every failure. No autonomous tool removal, retrieval, learned skill or physical hardware qualification is claimed.From reaching a shoe to keeping holdhttps://research.dvidia.org/papers/continuous-shoe-grip-v02/https://research.dvidia.org/papers/continuous-shoe-grip-v02/Tue, 13 Oct 2026 12:00:00 GMT20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified. A frozen twenty-attempt native MuJoCo comparison adds continuous acquisition checks and bounded lowering to the mounted-rake PiPER controller. Both policies complete the same 2/10 fresh cases: no gains or losses. Nine bilateral-contact-loss alarms follow a trace-verified native origin by 20–52 ms, median 30 ms; three acquisition failures are separate. Twelve supported lowering responses produce no additional completion. A non-finger-support gap remains unmonitored. Complete recorded trajectories, independent audit and development failures are public. Authored policies, synthetic contact and privileged observations establish no learned skill or physical robot qualification.Skill prerequisites: access, reach and recoveryhttps://research.dvidia.org/papers/skill-prerequisites-v01/https://research.dvidia.org/papers/skill-prerequisites-v01/Tue, 13 Oct 2026 12:00:00 GMT16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts. A frozen sixteen-attempt native MuJoCo study tests hinged shoe-box access and a mounted rake for an out-of-workspace shoe. Guarded access avoids two baseline lid collisions while task completion stays at 2/4; a transient withdrawal adds 0.70 simulated seconds without being necessary in its matched baseline. The mounted rake reaches an intermediate retrieval checkpoint in 3/4 cases, but all subsequent retained lifts fail and the short-tool case is deferred. All native traces and failures remain public. The browser replays recorded states; fresh physics runs locally. Privileged observations, synthetic contact and authored policies establish no learned skill or hardware qualification.Live PiPER shoe workstation v0.1https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/Tue, 13 Oct 2026 12:00:00 GMTLive browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified. A six-axis PiPER and its coupled gripper respond to user targets while MuJoCo runs locally in a browser worker. The final native run retains 20 valid attempts: 14/16 target trials pass, both high-target repeats fail, and jaw, impulse, disabled-table and 600-second hold checks pass. A ten-minute headless Chromium run reports 51.389 render calls per second and a 0.9999795 simulation/wall-time ratio on the tested Apple M5 host; simulated movement response is 61.9 ms p95 across 61 commands. Render calls do not measure GPU presentation. Native/WASM numerical and loaded-contact-count checks pass, while strict raw contact counts differ in 13/1,500 states. All fifteen development shoe-lift attempts fail. Complete development, failure and timing records remain separate. This authored rigid-shoe environment establishes no learned skill, calibrated material model or physical robot qualification.Reusable shoe asset library v0.1https://research.dvidia.org/papers/shoe-asset-library-v01/https://research.dvidia.org/papers/shoe-asset-library-v01/Mon, 12 Oct 2026 12:00:00 GMTSix shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified. Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.PiPER placement reliability v0.5https://research.dvidia.org/papers/piper-reliability-v05/https://research.dvidia.org/papers/piper-reliability-v05/Mon, 12 Oct 2026 12:00:00 GMT23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified. Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.PiPER contact recovery v0.4https://research.dvidia.org/papers/piper-contact-recovery-v04/https://research.dvidia.org/papers/piper-contact-recovery-v04/Sun, 11 Oct 2026 12:00:00 GMTAll modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified. A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.PiPER contact history v0.3https://research.dvidia.org/papers/piper-contact-history-v03/https://research.dvidia.org/papers/piper-contact-history-v03/Sun, 11 Oct 2026 12:00:00 GMTHistory 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified. A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.PiPER grip validation and recoveryhttps://research.dvidia.org/papers/piper-grip-validation-and-recovery/https://research.dvidia.org/papers/piper-grip-validation-and-recovery/Sun, 11 Oct 2026 12:00:00 GMTModerate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified. A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.Native grasp task benchmarkhttps://research.dvidia.org/papers/native-grasp-task-benchmark/https://research.dvidia.org/papers/native-grasp-task-benchmark/Sun, 11 Oct 2026 12:00:00 GMT6/6 simulated placements · controls 0/12 · real-time and hardware unqualified. A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.Hardware matched robot simulationhttps://research.dvidia.org/papers/hardware-matched-robot-simulation/https://research.dvidia.org/papers/hardware-matched-robot-simulation/Sun, 11 Oct 2026 12:00:00 GMTPinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured. A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.DVIDIA Observation Lab pilothttps://research.dvidia.org/papers/dvidia-observation-lab/https://research.dvidia.org/papers/dvidia-observation-lab/Sun, 11 Oct 2026 12:00:00 GMTLocal review release · motion and actuator source pilots · hardware unqualified. DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.Robotics research pipelinehttps://research.dvidia.org/papers/robotics-research-pipeline/https://research.dvidia.org/papers/robotics-research-pipeline/Fri, 09 Oct 2026 12:00:00 GMTArchitecture proposal · current simulation alpha · hardware unqualified. An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.Skillspace footage training pipelinehttps://research.dvidia.org/papers/skillspace-footage-training/https://research.dvidia.org/papers/skillspace-footage-training/Thu, 08 Oct 2026 12:00:00 GMTPublic CPU package · one native scene qualified · simulation-only. The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.Skill capsule acquisition and optimizationhttps://research.dvidia.org/papers/skill-capsule-acquisition/https://research.dvidia.org/papers/skill-capsule-acquisition/Wed, 07 Oct 2026 12:00:00 GMTSimulation-only · teacher/student 7/8 · no overall speedup. A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.Skillspace arm benchmarks and evolutionhttps://research.dvidia.org/papers/skillspace-arm-benchmarks/https://research.dvidia.org/papers/skillspace-arm-benchmarks/Wed, 07 Oct 2026 12:00:00 GMTMixed held-out result · 30/32 baseline, 29/32 updated. Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.Installing a Skillspace on a simulated armhttps://research.dvidia.org/papers/skillspace-arm-installation/https://research.dvidia.org/papers/skillspace-arm-installation/Wed, 07 Oct 2026 12:00:00 GMTPublic skill link to authored adapter · 6/6 scene placements. The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.Offline cable training environmenthttps://research.dvidia.org/papers/offline-cable-training-environment/https://research.dvidia.org/papers/offline-cable-training-environment/Wed, 07 Oct 2026 12:00:00 GMTCPU simulation-only · Controller search and frozen evaluation executed. The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.Fast accurate robot training simulationhttps://research.dvidia.org/papers/fast-accurate-robot-training-simulation/https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/Wed, 07 Oct 2026 12:00:00 GMT54 CPU fixture trials · GPU and physical transfer untested. A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.Grok robotics research on Xhttps://research.dvidia.org/papers/grok-robotics-research-on-x/https://research.dvidia.org/papers/grok-robotics-research-on-x/Wed, 07 Oct 2026 12:00:00 GMTPrimary sources checked · Experiments proposed. Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.Spatially grounded downloadable robot skillshttps://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/Wed, 07 Oct 2026 12:00:00 GMTSource review · DVIDIA experiments not run. A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.Clef and grounded video observationshttps://research.dvidia.org/papers/clef-and-grounded-video-observations/https://research.dvidia.org/papers/clef-and-grounded-video-observations/Tue, 06 Oct 2026 12:00:00 GMTFive licensed excerpts · Accuracy evaluation outstanding. A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.From Skillspace to robot skillhttps://research.dvidia.org/papers/from-skillspace-to-robot-skill/https://research.dvidia.org/papers/from-skillspace-to-robot-skill/Tue, 06 Oct 2026 12:00:00 GMTGraduation and robot training not run. An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.Turn physical footage into testable evidencehttps://research.dvidia.org/papers/physical-skill-research-agenda/https://research.dvidia.org/papers/physical-skill-research-agenda/Tue, 06 Oct 2026 12:00:00 GMTFour baseline protocols proposed. A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct. diff --git a/docs/index.html b/docs/index.html index 78e8e79..9d938d9 100644 --- a/docs/index.html +++ b/docs/index.html @@ -1,4 +1,4 @@ Research journal · DVIDIA Research -

An open research notebook

From observation
to physical skill.

We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.

Published by DVIDIA · hello@dvidia.org

Publications

25 papers and working notes

· Research direction and passive native contact audit

A visual goal still needs physical feedback

A goal image specifies a destination; physical feedback verifies whether a robot gets there. We describe a bounded decision role for TypeSafe Jev, distinguish it from action-conditioned world-model planning, and propose an independently scored reaching experiment. A separate passive audit reproduces fourteen previously inspected rigid-box arm episodes exactly and records 249,502 native ticks. Millisecond contact gaps hidden between stored observations become visible, while task outcomes remain unchanged. Complete inputs, traces, source snapshots and a verified downloader preserve every case. This is measurement work, not a new policy benchmark, PiPER shoe result or hardware qualification.

14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked

· Frozen shape, load and attached-tool diagnostic

Give the gripper a fair test

A frozen 24-attempt PiPER diagnostic separates three rigid shapes, 30 g and 300 g loads, and mounted versus absent rake conditions. All attempts are valid; eight of twelve lightweight controls retain a direct lift while every 300 g attempt fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result, with no net completion gain. A verified settled reference, dense contact traces, unchanged drive fingerprint and independent scoring expose limits in the modeled grasp and force response. Recorded replays and runnable CPU code retain every failure. No autonomous tool removal, retrieval, learned skill or physical hardware qualification is claimed.

24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake

· Frozen native comparison with independent contact audit

From reaching a shoe to keeping hold

A frozen twenty-attempt native MuJoCo comparison adds continuous acquisition checks and bounded lowering to the mounted-rake PiPER controller. Both policies complete the same 2/10 fresh cases: no gains or losses. Nine bilateral-contact-loss alarms follow a trace-verified native origin by 20–52 ms, median 30 ms; three acquisition failures are separate. Twelve supported lowering responses produce no additional completion. A non-finger-support gap remains unmonitored. Complete recorded trajectories, independent audit and development failures are public. Authored policies, synthetic contact and privileged observations establish no learned skill or physical robot qualification.

20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified

· Two native environments with authored policies and recorded benchmark evidence

Skill prerequisites: access, reach and recovery

A frozen sixteen-attempt native MuJoCo study tests hinged shoe-box access and a mounted rake for an out-of-workspace shoe. Guarded access avoids two baseline lid collisions while task completion stays at 2/4; a transient withdrawal adds 0.70 simulated seconds without being necessary in its matched baseline. The mounted rake reaches an intermediate retrieval checkpoint in 3/4 cases, but all subsequent retained lifts fail and the short-tool case is deferred. All native traces and failures remain public. The browser replays recorded states; fresh physics runs locally. Privileged observations, synthetic contact and authored policies establish no learned skill or hardware qualification.

16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts

· Live browser-native simulation with authored control and separate benchmark evidence

Live PiPER shoe workstation v0.1

A six-axis PiPER and its coupled gripper respond to user targets while MuJoCo runs locally in a browser worker. The final native run retains 20 valid attempts: 14/16 target trials pass, both high-target repeats fail, and jaw, impulse, disabled-table and 600-second hold checks pass. A ten-minute headless Chromium run reports 51.389 render calls per second and a 0.9999795 simulation/wall-time ratio on the tested Apple M5 host; simulated movement response is 61.9 ms p95 across 61 commands. Render calls do not measure GPU presentation. Native/WASM numerical and loaded-contact-count checks pass, while strict raw contact counts differ in 13/1,500 states. All fifteen development shoe-lift attempts fail. Complete development, failure and timing records remain separate. This authored rigid-shoe environment establishes no learned skill, calibrated material model or physical robot qualification.

Live browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified

· Versioned native scene assets and frozen passive-contact benchmark

Reusable shoe asset library v0.1

Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.

Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified

· Frozen paired reliability experiment with complete native evidence

PiPER placement reliability v0.5

Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.

23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified

· Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt

PiPER contact recovery v0.4

A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.

All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified

· Paired probe-only and contact-history experiment with complete confirmation receipt

PiPER contact history v0.3

A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.

History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified

· Paired authored controller experiment and complete confirmation receipt

PiPER grip validation and recovery

A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.

Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified

· Native contact task implementation and authored baseline

Native grasp task benchmark

A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.

6/6 simulated placements · controls 0/12 · real-time and hardware unqualified

· Pinned mechanics implementation and CPU numerical benchmark

Hardware matched robot simulation

A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.

Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured

· Local observation workflow and engineering feasibility pilot

DVIDIA Observation Lab pilot

DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.

Local review release · motion and actuator source pilots · hardware unqualified

· Architecture proposal and staged validation roadmap

Robotics research pipeline

An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.

Architecture proposal · current simulation alpha · hardware unqualified

· Product implementation and measured synthetic pilot

Skillspace footage training pipeline

The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.

Public CPU package · one native scene qualified · simulation-only

· Learned movement artifact and installation prototype

Skill capsule acquisition and optimization

A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.

Simulation-only · teacher/student 7/8 · no overall speedup

· Paired simulation benchmark and product assessment

Skillspace arm benchmarks and evolution

Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.

Mixed held-out result · 30/32 baseline, 29/32 updated

· Product prototype and native contact qualification

Installing a Skillspace on a simulated arm

The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.

Public skill link to authored adapter · 6/6 scene placements

· Product prototype and local qualification

Offline cable training environment

The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.

CPU simulation-only · Controller search and frozen evaluation executed

· Decision report and local experiment

Fast accurate robot training simulation

A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.

54 CPU fixture trials · GPU and physical transfer untested

· Source review and thesis comparison

Grok robotics research on X

Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.

Primary sources checked · Experiments proposed

· Research synthesis and proposed experiments

Spatially grounded downloadable robot skills

A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.

Source review · DVIDIA experiments not run

· Engineering feasibility pilot

Clef and grounded video observations

A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.

Five licensed excerpts · Accuracy evaluation outstanding

· Source review and implementation proposal

From Skillspace to robot skill

An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.

Graduation and robot training not run

· Research agenda

Turn physical footage into testable evidence

A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct.

Four baseline protocols proposed

Research footprints

  1. Connect visual goals to measured physical feedback

    Fourteen passive rigid-box arm replays reproduce every original sampled state and outcome, recording 249,502 native ticks. Millisecond contact gaps appear between stored observations; a successful comparator also has gaps. The separate architecture note evaluates Jev as a candidate decision layer and defines a goal-image reaching test. No visual model is integrated and no controller is promoted.

    Read the architecture and audit · Download verified evidence tool

  2. Separate object difficulty from the gripper and attached tool

    A frozen direct-grasp diagnostic retains 24 valid attempts. Lightweight controls complete 8/12 lifts; every 300 g condition fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result. Relative lift, settled references and dense contact traces make the modeled drive and geometry limits visible. These are diagnostic controls, not a shoe retrieval rate or hardware qualification.

    Read the graspability study · Inspect every recorded attempt

  3. Measure contact-loss response without claiming a better grasp

    Twenty valid fresh attempts complete the same 2/10 cases under both controllers. Nine trace-verified bilateral-contact-loss alarms arrive in 20–52 ms; twelve supported lowering responses produce no additional completion. Full trajectories and an independent audit expose failed acquisition and an unmonitored support-contamination gap. Hardware transfer remains open.

    Read the continuous-grip comparison · Inspect the recorded attempts

  4. Make skill prerequisites and failed recovery measurable

    Two native environments retain sixteen valid held-out attempts. A lid guard avoids two baseline contacts while completion remains 2/4. A mounted rake reaches three of four intermediate retrieval checkpoints, but no subsequent retained lift succeeds. Recorded replays expose every failure; fresh physics runs locally. Tool acquisition, learned control and hardware transfer remain open.

    Read the paired results · Explore both recorded experiments

  5. Move the PiPER arm in a live browser workstation

    Drag a target and nudge a free rigid trainer with live browser MuJoCo physics. The final native run keeps 20 valid attempts: 14/16 targets pass, both high-target repeats fail, and four mechanics checks pass. A ten-minute browser run reports 51.39 render calls per second on the tested host. Strict raw-contact parity fails in 13/1,500 sampled states despite numerical agreement; all fifteen development shoe-lift attempts fail. Learned shoe skills, calibrated contact and hardware transfer remain open.

    Read the workstation and evidence · Open the live workstation

  6. Load reusable shoes with three native robot arms

    Six original rigid shoe proxies now compose with UR5e/Robotiq, PiPER and SO-101. Versioned scene files, offline 3D previews and a frozen 54-attempt benchmark retain every trajectory: supported pairs 27/27, disabled-table support 0/27, all gravity checks pass. Native loading and passive contact are tested; shoe grasping, deformation and physical transfer remain unqualified.

    Read the asset library results · Browse shoes and arms

  7. Complete more placements with a bounded second lift and revised grasp

    Three fresh cohorts retain 252 valid attempts. The revised grasp completes 23/36 placements versus 15/36 for v0.4 and 18/36 with second lift alone; all 144 controls fail. Eight completions are gained and none lost, while shared successes average 0.772 simulated seconds slower. Twelve low-friction acquisitions and one retention failure remain. All 1,035 computation overruns are retained; physical qualification is open.

    Read the v0.5 reliability comparison · Inspect all three cohorts

  8. Separate faster recovery from an ineffective retry

    A fresh 84-attempt comparison completes 4/12 placements in every mode; all 48 controls fail. Two resumed tasks finish 2.440–2.640 simulated seconds sooner after unchanged settling holds, while four failed grips are refused 2.88–2.90 seconds sooner. Eight failures remain, including two held blocks below the resume-height gate. Every native trajectory and raw computation duration is retained; physical qualification is open.

    Read the v0.4 recovery comparison · Inspect every paired attempt

  9. Compare probe-only motion with contact history

    A frozen 84-attempt paired cohort completes 6/12 history placements versus 4/12 original and probe-only placements; all 48 controls fail. Four tasks complete after five closed holds/resumes, but all six supported acquisition retries fail. Only four of five resume windows meet the independent low absolute tool-speed check. Actual motion and 75 missed computation windows remain visible; physical robot qualification is open.

    Read the contact history comparison · Inspect all 84 attempts

  10. Test the grip and publish a completion regression

    A slow probe and one supported retry retain 30 valid attempts. At moderate friction baseline completes 2/2 while guarded completes 0/2; normal cases both pass and low-friction cases both fail. No successful recovery or overall improvement is demonstrated. Four brief following windows do not establish detector accuracy; 53 computation windows miss 20 ms. Every full attempt remains public.

    Read the paired results and actual stop motion · Inspect all 30 attempts

  11. Measure PiPER contact and a friction failure

    A frozen authored rigid-block pilot retains nine valid attempts: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3. One of 9,000 computation windows exceeds 20 ms. Complete task receipts remain distinct from unloaded profiles; learned skills, shoe physics and physical qualification are open.

    Read the PiPER contact pilot · Open the Robot Simulation Lab

  12. Run more arms on a local CPU

    PiPER and SO-101 join the library with pinned models, explicit arm/gripper units, preserved licenses and complete numerical receipts. Each completes six unloaded motion checks offline. Their learned-policy and physical qualification remain open. The PiPER contact pilot and UR5e grasp demonstration have separate task receipts.

    Read the profile measurements · Open the Robot Simulation Lab

  13. Give the reference arm a contact task

    A free rigid block must be grasped, lifted, carried, released and left settled at its destination. Frozen scenarios compare the authored controller with identical arm commands and open jaws, and with a hold control. Full traces and 3D replay preserve the evidence for the next compact action-learning experiment. Shoe physics, a learned task policy and hardware transfer remain open.

    Read the native task benchmark · Replay grasp and placement

  14. Build a hardware reference we can measure

    A pinned six-axis arm and coupled gripper expose fourteen coordinates and seven commands. All 18 unloaded numerical runs are valid; 1 ms CPU physics measures 40.80–47.29× elapsed-time throughput with sensing, rendering and policy excluded. A paired trace scorer makes physical mismatch measurable. Five-finger references and LeRobot collection have separate contracts; hardware fidelity and shoe competence remain unmeasured.

    Read the mechanical contract and upcoming arms · Open the 3D replay

  15. Learn a compact command-to-motion predictor

    A 6,438-byte CPU model predicts the next 20 ms of joint/jaw movement from native state and issued commands. Twelve seeded groups retain 36 recordings; 4,553 test transitions are not independent trials. Warm computation measures 0.207 ms p95, but jaw mean absolute/worst errors regress against persistence and the first opening is overpredicted. Shoe collection, hardware sensing and validated shoe physics remain open.

    Read the measured pilot and shoe collection plan

  16. Observe image motion and forecast its path

    A manually seeded CPU source alpha records pixel speed and causal forecasts. Six authored cases process 720 frames in 1.454 seconds; occlusion and low-texture losses remain visible, and a 500 ms reversal forecast misses by nearly 90 pixels. The v0.1 release/demo remain separate; real-scene accuracy, metric speed and live robot control are unqualified.

    Read the source alpha and measured limitations

  17. Publish the robotics research pipeline

    A diagram connects footage review, measured robot state, local training, frozen simulation evaluation and compatible skill installation. Automatic annotation and a learned task supervisor are proposed; their companion validation studies and physical robot qualification remain unrun.

    Read the architecture and validation gates

  18. A public skill link reaches a simulated arm

    One-click local installation binds a DVIDIA placement envelope to an authored contact controller. Six scene layouts pass; identical joint commands with open jaws and no-motion controls each pass 0/6. Physical transfer and learned policies remain open.

    Read the installation contract and arm replay

  19. The offline cable environment is runnable

    A local controller-training loop and simulation-only skill pack now run on CPU. Trained and baseline feedback each pass 10/10 simulated seeds; zero force and early release each pass 0/10. Real gripping, calibration and GPU qualification remain open.

    Read the first product build and replay

  20. Contact accuracy meets runtime

    54 deterministic CPU fixture trials recorded. Endpoint agreement coexists with unqualified force transients; coarse wall tests expose missed contact. No GPU or physical-transfer claim.

    Read the measurements

  21. Compare the observation-to-skill thesis with prior work

    Spatial attention, demonstration coverage, executable simulation and reusable repairs are reviewed separately. Three parallel Grok 4.7 searches support discovery; primary sources support the claims.

    Read the comparison

  22. Five licensed POV excerpts processed

    Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.

    Read the feasibility run

  23. Define the evidence and release questions

    Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.

    Read the agenda

Open questions

  • Capture quality — Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.
  • Skill relations — Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.
  • Evaluation and provenance — Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.
  • Physical grounding — Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.

Follow new publications through RSS or inspect the source and revisions on GitHub.

+

An open research notebook

From observation
to physical skill.

We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.

Published by DVIDIA · hello@dvidia.org

Publications

26 papers and working notes

· Native diagnostic benchmark and frozen hosted-model protocol

Image-goal verification: native benchmark and Jev protocol

A frozen PiPER image-goal cohort crosses eight physical conditions with four layouts. Four moving/settled pairs have byte-identical images and different completion labels, exposing when one image cannot establish task completion. Native measurements verify 32 authored fixtures; a paired 64-request Jev protocol distinguishes RGB judgments from privileged simulator telemetry. The provider evaluation has not run because credentials were unavailable: accuracy, false-completion rates and service latency remain unmeasured. Every native state, image, frozen request hash and explicit unrun result is retained. No learned policy or hardware qualification is claimed.

32 native fixtures · 4 identical-image pairs · 64 Jev trials not run

· Research direction and passive native contact audit

A visual goal still needs physical feedback

A goal image specifies a destination; physical feedback verifies whether a robot gets there. We describe a bounded decision role for TypeSafe Jev, distinguish it from action-conditioned world-model planning, and propose an independently scored reaching experiment. A separate passive audit reproduces fourteen previously inspected rigid-box arm episodes exactly and records 249,502 native ticks. Millisecond contact gaps hidden between stored observations become visible, while task outcomes remain unchanged. Complete inputs, traces, source snapshots and a verified downloader preserve every case. This is measurement work, not a new policy benchmark, PiPER shoe result or hardware qualification.

14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked

· Frozen shape, load and attached-tool diagnostic

Give the gripper a fair test

A frozen 24-attempt PiPER diagnostic separates three rigid shapes, 30 g and 300 g loads, and mounted versus absent rake conditions. All attempts are valid; eight of twelve lightweight controls retain a direct lift while every 300 g attempt fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result, with no net completion gain. A verified settled reference, dense contact traces, unchanged drive fingerprint and independent scoring expose limits in the modeled grasp and force response. Recorded replays and runnable CPU code retain every failure. No autonomous tool removal, retrieval, learned skill or physical hardware qualification is claimed.

24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake

· Frozen native comparison with independent contact audit

From reaching a shoe to keeping hold

A frozen twenty-attempt native MuJoCo comparison adds continuous acquisition checks and bounded lowering to the mounted-rake PiPER controller. Both policies complete the same 2/10 fresh cases: no gains or losses. Nine bilateral-contact-loss alarms follow a trace-verified native origin by 20–52 ms, median 30 ms; three acquisition failures are separate. Twelve supported lowering responses produce no additional completion. A non-finger-support gap remains unmonitored. Complete recorded trajectories, independent audit and development failures are public. Authored policies, synthetic contact and privileged observations establish no learned skill or physical robot qualification.

20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified

· Two native environments with authored policies and recorded benchmark evidence

Skill prerequisites: access, reach and recovery

A frozen sixteen-attempt native MuJoCo study tests hinged shoe-box access and a mounted rake for an out-of-workspace shoe. Guarded access avoids two baseline lid collisions while task completion stays at 2/4; a transient withdrawal adds 0.70 simulated seconds without being necessary in its matched baseline. The mounted rake reaches an intermediate retrieval checkpoint in 3/4 cases, but all subsequent retained lifts fail and the short-tool case is deferred. All native traces and failures remain public. The browser replays recorded states; fresh physics runs locally. Privileged observations, synthetic contact and authored policies establish no learned skill or hardware qualification.

16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts

· Live browser-native simulation with authored control and separate benchmark evidence

Live PiPER shoe workstation v0.1

A six-axis PiPER and its coupled gripper respond to user targets while MuJoCo runs locally in a browser worker. The final native run retains 20 valid attempts: 14/16 target trials pass, both high-target repeats fail, and jaw, impulse, disabled-table and 600-second hold checks pass. A ten-minute headless Chromium run reports 51.389 render calls per second and a 0.9999795 simulation/wall-time ratio on the tested Apple M5 host; simulated movement response is 61.9 ms p95 across 61 commands. Render calls do not measure GPU presentation. Native/WASM numerical and loaded-contact-count checks pass, while strict raw contact counts differ in 13/1,500 states. All fifteen development shoe-lift attempts fail. Complete development, failure and timing records remain separate. This authored rigid-shoe environment establishes no learned skill, calibrated material model or physical robot qualification.

Live browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified

· Versioned native scene assets and frozen passive-contact benchmark

Reusable shoe asset library v0.1

Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.

Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified

· Frozen paired reliability experiment with complete native evidence

PiPER placement reliability v0.5

Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.

23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified

· Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt

PiPER contact recovery v0.4

A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.

All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified

· Paired probe-only and contact-history experiment with complete confirmation receipt

PiPER contact history v0.3

A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.

History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified

· Paired authored controller experiment and complete confirmation receipt

PiPER grip validation and recovery

A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.

Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified

· Native contact task implementation and authored baseline

Native grasp task benchmark

A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.

6/6 simulated placements · controls 0/12 · real-time and hardware unqualified

· Pinned mechanics implementation and CPU numerical benchmark

Hardware matched robot simulation

A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.

Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured

· Local observation workflow and engineering feasibility pilot

DVIDIA Observation Lab pilot

DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.

Local review release · motion and actuator source pilots · hardware unqualified

· Architecture proposal and staged validation roadmap

Robotics research pipeline

An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.

Architecture proposal · current simulation alpha · hardware unqualified

· Product implementation and measured synthetic pilot

Skillspace footage training pipeline

The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.

Public CPU package · one native scene qualified · simulation-only

· Learned movement artifact and installation prototype

Skill capsule acquisition and optimization

A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.

Simulation-only · teacher/student 7/8 · no overall speedup

· Paired simulation benchmark and product assessment

Skillspace arm benchmarks and evolution

Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.

Mixed held-out result · 30/32 baseline, 29/32 updated

· Product prototype and native contact qualification

Installing a Skillspace on a simulated arm

The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.

Public skill link to authored adapter · 6/6 scene placements

· Product prototype and local qualification

Offline cable training environment

The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.

CPU simulation-only · Controller search and frozen evaluation executed

· Decision report and local experiment

Fast accurate robot training simulation

A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.

54 CPU fixture trials · GPU and physical transfer untested

· Source review and thesis comparison

Grok robotics research on X

Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.

Primary sources checked · Experiments proposed

· Research synthesis and proposed experiments

Spatially grounded downloadable robot skills

A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.

Source review · DVIDIA experiments not run

· Engineering feasibility pilot

Clef and grounded video observations

A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.

Five licensed excerpts · Accuracy evaluation outstanding

· Source review and implementation proposal

From Skillspace to robot skill

An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.

Graduation and robot training not run

· Research agenda

Turn physical footage into testable evidence

A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct.

Four baseline protocols proposed

Research footprints

  1. Make image-goal uncertainty testable

    Thirty-two native PiPER fixtures include four moving/settled pairs with identical images and different physical completion labels. The frozen 64-request Jev protocol compares RGB with privileged simulator telemetry. Jev has not been run; all provider trials remain explicitly unrun, with accuracy and service latency unmeasured. Native states, images, source hashes and physical labels are public.

    Read the benchmark and protocol · Compare every current and goal image

  2. Connect visual goals to measured physical feedback

    Fourteen passive rigid-box arm replays reproduce every original sampled state and outcome, recording 249,502 native ticks. Millisecond contact gaps appear between stored observations; a successful comparator also has gaps. The separate architecture note evaluates Jev as a candidate decision layer and defines a goal-image reaching test. No visual model is integrated and no controller is promoted.

    Read the architecture and audit · Download verified evidence tool

  3. Separate object difficulty from the gripper and attached tool

    A frozen direct-grasp diagnostic retains 24 valid attempts. Lightweight controls complete 8/12 lifts; every 300 g condition fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result. Relative lift, settled references and dense contact traces make the modeled drive and geometry limits visible. These are diagnostic controls, not a shoe retrieval rate or hardware qualification.

    Read the graspability study · Inspect every recorded attempt

  4. Measure contact-loss response without claiming a better grasp

    Twenty valid fresh attempts complete the same 2/10 cases under both controllers. Nine trace-verified bilateral-contact-loss alarms arrive in 20–52 ms; twelve supported lowering responses produce no additional completion. Full trajectories and an independent audit expose failed acquisition and an unmonitored support-contamination gap. Hardware transfer remains open.

    Read the continuous-grip comparison · Inspect the recorded attempts

  5. Make skill prerequisites and failed recovery measurable

    Two native environments retain sixteen valid held-out attempts. A lid guard avoids two baseline contacts while completion remains 2/4. A mounted rake reaches three of four intermediate retrieval checkpoints, but no subsequent retained lift succeeds. Recorded replays expose every failure; fresh physics runs locally. Tool acquisition, learned control and hardware transfer remain open.

    Read the paired results · Explore both recorded experiments

  6. Move the PiPER arm in a live browser workstation

    Drag a target and nudge a free rigid trainer with live browser MuJoCo physics. The final native run keeps 20 valid attempts: 14/16 targets pass, both high-target repeats fail, and four mechanics checks pass. A ten-minute browser run reports 51.39 render calls per second on the tested host. Strict raw-contact parity fails in 13/1,500 sampled states despite numerical agreement; all fifteen development shoe-lift attempts fail. Learned shoe skills, calibrated contact and hardware transfer remain open.

    Read the workstation and evidence · Open the live workstation

  7. Load reusable shoes with three native robot arms

    Six original rigid shoe proxies now compose with UR5e/Robotiq, PiPER and SO-101. Versioned scene files, offline 3D previews and a frozen 54-attempt benchmark retain every trajectory: supported pairs 27/27, disabled-table support 0/27, all gravity checks pass. Native loading and passive contact are tested; shoe grasping, deformation and physical transfer remain unqualified.

    Read the asset library results · Browse shoes and arms

  8. Complete more placements with a bounded second lift and revised grasp

    Three fresh cohorts retain 252 valid attempts. The revised grasp completes 23/36 placements versus 15/36 for v0.4 and 18/36 with second lift alone; all 144 controls fail. Eight completions are gained and none lost, while shared successes average 0.772 simulated seconds slower. Twelve low-friction acquisitions and one retention failure remain. All 1,035 computation overruns are retained; physical qualification is open.

    Read the v0.5 reliability comparison · Inspect all three cohorts

  9. Separate faster recovery from an ineffective retry

    A fresh 84-attempt comparison completes 4/12 placements in every mode; all 48 controls fail. Two resumed tasks finish 2.440–2.640 simulated seconds sooner after unchanged settling holds, while four failed grips are refused 2.88–2.90 seconds sooner. Eight failures remain, including two held blocks below the resume-height gate. Every native trajectory and raw computation duration is retained; physical qualification is open.

    Read the v0.4 recovery comparison · Inspect every paired attempt

  10. Compare probe-only motion with contact history

    A frozen 84-attempt paired cohort completes 6/12 history placements versus 4/12 original and probe-only placements; all 48 controls fail. Four tasks complete after five closed holds/resumes, but all six supported acquisition retries fail. Only four of five resume windows meet the independent low absolute tool-speed check. Actual motion and 75 missed computation windows remain visible; physical robot qualification is open.

    Read the contact history comparison · Inspect all 84 attempts

  11. Test the grip and publish a completion regression

    A slow probe and one supported retry retain 30 valid attempts. At moderate friction baseline completes 2/2 while guarded completes 0/2; normal cases both pass and low-friction cases both fail. No successful recovery or overall improvement is demonstrated. Four brief following windows do not establish detector accuracy; 53 computation windows miss 20 ms. Every full attempt remains public.

    Read the paired results and actual stop motion · Inspect all 30 attempts

  12. Measure PiPER contact and a friction failure

    A frozen authored rigid-block pilot retains nine valid attempts: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3. One of 9,000 computation windows exceeds 20 ms. Complete task receipts remain distinct from unloaded profiles; learned skills, shoe physics and physical qualification are open.

    Read the PiPER contact pilot · Open the Robot Simulation Lab

  13. Run more arms on a local CPU

    PiPER and SO-101 join the library with pinned models, explicit arm/gripper units, preserved licenses and complete numerical receipts. Each completes six unloaded motion checks offline. Their learned-policy and physical qualification remain open. The PiPER contact pilot and UR5e grasp demonstration have separate task receipts.

    Read the profile measurements · Open the Robot Simulation Lab

  14. Give the reference arm a contact task

    A free rigid block must be grasped, lifted, carried, released and left settled at its destination. Frozen scenarios compare the authored controller with identical arm commands and open jaws, and with a hold control. Full traces and 3D replay preserve the evidence for the next compact action-learning experiment. Shoe physics, a learned task policy and hardware transfer remain open.

    Read the native task benchmark · Replay grasp and placement

  15. Build a hardware reference we can measure

    A pinned six-axis arm and coupled gripper expose fourteen coordinates and seven commands. All 18 unloaded numerical runs are valid; 1 ms CPU physics measures 40.80–47.29× elapsed-time throughput with sensing, rendering and policy excluded. A paired trace scorer makes physical mismatch measurable. Five-finger references and LeRobot collection have separate contracts; hardware fidelity and shoe competence remain unmeasured.

    Read the mechanical contract and upcoming arms · Open the 3D replay

  16. Learn a compact command-to-motion predictor

    A 6,438-byte CPU model predicts the next 20 ms of joint/jaw movement from native state and issued commands. Twelve seeded groups retain 36 recordings; 4,553 test transitions are not independent trials. Warm computation measures 0.207 ms p95, but jaw mean absolute/worst errors regress against persistence and the first opening is overpredicted. Shoe collection, hardware sensing and validated shoe physics remain open.

    Read the measured pilot and shoe collection plan

  17. Observe image motion and forecast its path

    A manually seeded CPU source alpha records pixel speed and causal forecasts. Six authored cases process 720 frames in 1.454 seconds; occlusion and low-texture losses remain visible, and a 500 ms reversal forecast misses by nearly 90 pixels. The v0.1 release/demo remain separate; real-scene accuracy, metric speed and live robot control are unqualified.

    Read the source alpha and measured limitations

  18. Publish the robotics research pipeline

    A diagram connects footage review, measured robot state, local training, frozen simulation evaluation and compatible skill installation. Automatic annotation and a learned task supervisor are proposed; their companion validation studies and physical robot qualification remain unrun.

    Read the architecture and validation gates

  19. A public skill link reaches a simulated arm

    One-click local installation binds a DVIDIA placement envelope to an authored contact controller. Six scene layouts pass; identical joint commands with open jaws and no-motion controls each pass 0/6. Physical transfer and learned policies remain open.

    Read the installation contract and arm replay

  20. The offline cable environment is runnable

    A local controller-training loop and simulation-only skill pack now run on CPU. Trained and baseline feedback each pass 10/10 simulated seeds; zero force and early release each pass 0/10. Real gripping, calibration and GPU qualification remain open.

    Read the first product build and replay

  21. Contact accuracy meets runtime

    54 deterministic CPU fixture trials recorded. Endpoint agreement coexists with unqualified force transients; coarse wall tests expose missed contact. No GPU or physical-transfer claim.

    Read the measurements

  22. Compare the observation-to-skill thesis with prior work

    Spatial attention, demonstration coverage, executable simulation and reusable repairs are reviewed separately. Three parallel Grok 4.7 searches support discovery; primary sources support the claims.

    Read the comparison

  23. Five licensed POV excerpts processed

    Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.

    Read the feasibility run

  24. Define the evidence and release questions

    Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.

    Read the agenda

Open questions

  • Capture quality — Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.
  • Skill relations — Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.
  • Evaluation and provenance — Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.
  • Physical grounding — Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.

Follow new publications through RSS or inspect the source and revisions on GitHub.

diff --git a/docs/papers/clef-and-grounded-video-observations/index.html b/docs/papers/clef-and-grounded-video-observations/index.html index 8dac562..90be979 100644 --- a/docs/papers/clef-and-grounded-video-observations/index.html +++ b/docs/papers/clef-and-grounded-video-observations/index.html @@ -1,7 +1,7 @@ Clef and grounded video observations · DVIDIA Research -

Engineering feasibility pilot

Clef and grounded video observations

· DVIDIA

Five licensed excerpts · Accuracy evaluation outstanding

Source & history · Download Markdown

Abstract

A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.

In this paper

Engineering pilot executed · 6 October 2026 · Accuracy evaluation outstanding

+

Engineering feasibility pilot

Clef and grounded video observations

· DVIDIA

Five licensed excerpts · Accuracy evaluation outstanding

Source & history · Download Markdown

Abstract

A preregistered image-API feasibility run processed five licensed three-second POV excerpts using four timestamped frames, a Gemma caption and a Clef-flash check. All five completed at a price-derived inference estimate of $0.00140634. Post-run inspection found an object error accepted by the checker and omitted state changes. API and schema compatibility were demonstrated; annotation accuracy and whole-task success were not.

In this paper

Engineering pilot executed · 6 October 2026 · Accuracy evaluation outstanding

We executed a small, preregistered feasibility run: five existing public, licensed three-second POV excerpts, four original-timestamped frames per excerpt, one Gemma caption call and one Clef-flash check per excerpt. All five completed. The estimated inference cost was $0.00140634. This establishes that the image APIs and bounded data contract work; it does not establish that the annotations are accurate.

The useful finding was a failure: a caption called a pink plate a cutting board, and the second model agreed. Several descriptions listed objects or hand positions while missing visible folding or transfer. Model agreement must not become a “verified” badge. The observations below come from AI-assisted inspection of the exact input frames after outputs were frozen, not independent human annotations.

Preregistration · Complete sanitized results and source/frame hashes · Prompts and request recipes · Next evaluation protocol

diff --git a/docs/papers/dvidia-observation-lab/index.html b/docs/papers/dvidia-observation-lab/index.html index 136b1e8..9f25e09 100644 --- a/docs/papers/dvidia-observation-lab/index.html +++ b/docs/papers/dvidia-observation-lab/index.html @@ -1,7 +1,7 @@ DVIDIA Observation Lab pilot · DVIDIA Research -

Local observation workflow and engineering feasibility pilot

DVIDIA Observation Lab pilot

· DVIDIA

Local review release · motion and actuator source pilots · hardware unqualified

Source & history · Download Markdown

Abstract

DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.

In this paper

Engineering integration passed on six public excerpts; annotation accuracy remains unmeasured. On 10 October 2026, we ran a local observation pipeline that decodes video, proposes sampled object and palm boxes, associates nearby detections and opens an editable review timeline. Review changes persist as revisions and export with their source identity. This establishes a working research tool, not reliable automatic task labeling, a cup-placement benchmark or a trained robot skill. The published implementation is anchored to DVIDIA Training commit d355a4a.

+

Local observation workflow and engineering feasibility pilot

DVIDIA Observation Lab pilot

· DVIDIA

Local review release · motion and actuator source pilots · hardware unqualified

Source & history · Download Markdown

Abstract

DVIDIA Observation Lab supplies local object/palm proposals, reviewed timeline revisions and separate causal image-motion forecasts; real annotation and motion accuracy remain unmeasured. A new CPU pilot learns next-20-ms joint/jaw deltas from current native state and issued commands. Twelve seeded groups retain 36 recordings with 8/2/2 train/development/test grouping; 4,553 test transitions are not independent robot trials. The 6,438-byte action model computes at 0.207 ms p95 on one host and improves RMSE, while jaw MAE/worst error regress against persistence and an initial opening is substantially overpredicted. These are privileged simulator measurements and model-only timings. A proposed shoe capture plan has no footage or executed shoe benchmark. The v0.1 release and hosted synthetic review demo remain separate; no shoe policy, physical adapter, validated shoe physics or hardware qualification is established.

In this paper

Engineering integration passed on six public excerpts; annotation accuracy remains unmeasured. On 10 October 2026, we ran a local observation pipeline that decodes video, proposes sampled object and palm boxes, associates nearby detections and opens an editable review timeline. Review changes persist as revisions and export with their source identity. This establishes a working research tool, not reliable automatic task labeling, a cup-placement benchmark or a trained robot skill. The published implementation is anchored to DVIDIA Training commit d355a4a.

Install the Observation Lab release · GitHub release and downloads · Try the synthetic review demo · Frozen engineering receipts

The pilot produces reviewable 2D evidence

The local adapter uses the pinned OpenCV Zoo YOLOX COCO object detector and MediaPipe palm detector. It samples at two frames per second, up to 120 frames and a 640-pixel maximum image side per clip. Proposals retain decoded presentation timestamps, frame and source hashes, raw labels and scores. Same-class image overlap associates detections; palm/object overlap proposes 2D proximity. Neither association establishes physical identity nor overlap proves touching. Palm rectangles are not full-hand segmentation. No optical flow, depth reconstruction, natural-language action captioner or language model participates in this pilot. (Observation contract, frozen model and recipe)

diff --git a/docs/papers/fast-accurate-robot-training-simulation/index.html b/docs/papers/fast-accurate-robot-training-simulation/index.html index e3c8fce..5d9a902 100644 --- a/docs/papers/fast-accurate-robot-training-simulation/index.html +++ b/docs/papers/fast-accurate-robot-training-simulation/index.html @@ -1,7 +1,7 @@ Fast accurate robot training simulation · DVIDIA Research -

Decision report and local experiment

Fast accurate robot training simulation

· DVIDIA

54 CPU fixture trials · GPU and physical transfer untested

Source & history · Download Markdown

Abstract

A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.

In this paper

Decision report · 7 October 2026 · Grok 4.7 research plus primary-source verification · 54 local CPU fixture trials; no GPU training or physical robot tests

+

Decision report and local experiment

Fast accurate robot training simulation

· DVIDIA

54 CPU fixture trials · GPU and physical transfer untested

Source & history · Download Markdown

Abstract

A design for an offline, self-hosted training environment that turns demonstrations into calibrated practice and qualified robot skills. We examine contact, deformation, sensor feedback and the performance–accuracy tradeoff, then report 54 deterministic native MuJoCo CPU fixture trials. Fine endpoint agreement did not establish accurate contact transients, and coarse timesteps missed collisions. Minimum GPU memory, rope accuracy and physical transfer remain unmeasured.

In this paper

Decision report · 7 October 2026 · Grok 4.7 research plus primary-source verification · 54 local CPU fixture trials; no GPU training or physical robot tests

Build an offline, self-hosted environment that converts demonstrations into calibrated practice and qualified robot skills. Start with existing physics engines and concentrate the product on task construction, uncertainty, failure/recovery coverage, efficient scheduling and qualification. Use native MuJoCo for local numerical checks, test MuJoCo Warp for batched rigid contact, and qualify Newton rods or another deformable backend against the actual rope task before selecting it. The microscopic intuition explains why objects resist and deform; the useful simulation interface is the resulting force, motion, friction and sensor response at robot scale. The first product vertical should be bounded rope grasping, pulling, threading and regrasping, with bow tying a later capability. One owned GPU is the starting design target; minimum useful VRAM and an “ultrafast” training rate remain unmeasured. Adding GPUs or servers should scale independent practice worlds, while a released skill retains a declared hardware profile and physical evidence.

Reproduce the response that changes the robot's decision

When a fingertip presses a table, resistance ultimately comes from electromagnetic interactions and quantum constraints on matter. “Electrons reject each other” is a useful intuition but incomplete: atoms also attract and bond, and Pauli exclusion restricts allowed electron states rather than adding a separate classical repulsive force. Molecular structure and collective interactions produce elasticity. We do not need to track electrons to calculate whether a gripper holds a lace; we need the effective mechanical behavior at the force, length and time scales the task uses. (Feynman on forces and elasticity, exclusion and matter)

diff --git a/docs/papers/from-skillspace-to-robot-skill/index.html b/docs/papers/from-skillspace-to-robot-skill/index.html index aec70f3..b447d06 100644 --- a/docs/papers/from-skillspace-to-robot-skill/index.html +++ b/docs/papers/from-skillspace-to-robot-skill/index.html @@ -1,7 +1,7 @@ From Skillspace to robot skill · DVIDIA Research -

Source review and implementation proposal

From Skillspace to robot skill

· DVIDIA

Graduation and robot training not run

Source & history · Download Markdown

Abstract

An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.

In this paper

Source review and proposal · 6 October 2026 · Graduation experiment not run

+

Source review and implementation proposal

From Skillspace to robot skill

· DVIDIA

Graduation and robot training not run

Source & history · Download Markdown

Abstract

An Argus review and a proposal to connect original recordings, reviewed episodes, immutable datasets, training runs, held-out evaluation and robot-tested releases. The review separates video annotation from action supervision and defines compatibility beyond a robot’s degree-of-freedom count. A proposed 40-episode annotation pilot and evidence-based graduation criteria remain unrun.

In this paper

Source review and proposal · 6 October 2026 · Graduation experiment not run

Follow-up: the Clef image feasibility pilot has now run on five public excerpts. It does not complete the 40-episode accuracy protocol below.

A Skillspace can become the home for a skill's learning history. More uploaded videos do not, by themselves, establish a better policy. We need evidence that new demonstrations add useful coverage, survive quality checks, and improve held-out performance on a specified robot.

This review follows Varun Nair's pipeline post, its linked Argus article, selected upstream source files, and an audit of the DVIDIA website. The design below is our proposal, not an implemented training service. No user footage was sent to any model, no training was run, and no robot was operated for this review.

diff --git a/docs/papers/grok-robotics-research-on-x/index.html b/docs/papers/grok-robotics-research-on-x/index.html index cf7bd67..db72f43 100644 --- a/docs/papers/grok-robotics-research-on-x/index.html +++ b/docs/papers/grok-robotics-research-on-x/index.html @@ -1,7 +1,7 @@ Grok robotics research on X · DVIDIA Research -

Source review and thesis comparison

Grok robotics research on X

· DVIDIA

Primary sources checked · Experiments proposed

Source & history · Download Markdown

Abstract

Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.

In this paper

Research snapshot: 7 October 2026 · Grok 4.7 used through Pi CLI · No training, simulation or hardware experiment was run

+

Source review and thesis comparison

Grok robotics research on X

· DVIDIA

Primary sources checked · Experiments proposed

Source & history · Download Markdown

Abstract

Three Grok 4.7 research passes surface observation-to-action, synthetic-practice and reusable-repair ideas. Independent primary-source checks compare those findings with our thesis: observation supplies goals and state, language retrieves a skill, spatial expectations guide attention, and executable feedback with evaluation establishes competence. X posts remain discovery leads. No universal demonstration count or arbitrary-robot transfer follows from the reviewed results.

In this paper

Research snapshot: 7 October 2026 · Grok 4.7 used through Pi CLI · No training, simulation or hardware experiment was run

Your initial thesis has substantial support when expressed as observation-derived goals and task state, language-based retrieval, contextual spatial attention, and simulation-assisted learning of a feedback skill. The evidence does not establish that a fixed quantity of video covers every variant, that XYZ determines what to do, or that a simulated policy downloads unchanged onto arbitrary robots. The strongest interpretation of your small-sample idea is adaptation: existing models supply reusable manipulation knowledge, while new demonstrations specify a task, object or operating region. Recent work makes that interpretation more credible, and also exposes its dependencies. Robots can learn through trial and error and retain useful repairs; execution still needs observable outcomes and compatible actions. A fast virtual training environment is consequently a plausible product hypothesis, but the research first needs to establish which observed information makes training more efficient and which simulated improvements survive physical transfer.

The observation-to-skill thesis matches the evidence when each skill has a tested operating region

The following mapping preserves the sequence you described. “Supported” means a component has demonstrated precedents, not that the complete proposed architecture has been demonstrated.

diff --git a/docs/papers/hardware-matched-robot-simulation/index.html b/docs/papers/hardware-matched-robot-simulation/index.html index d357ebb..baa40d1 100644 --- a/docs/papers/hardware-matched-robot-simulation/index.html +++ b/docs/papers/hardware-matched-robot-simulation/index.html @@ -1,7 +1,7 @@ Hardware matched robot simulation · DVIDIA Research -

Pinned mechanics implementation and CPU numerical benchmark

Hardware matched robot simulation

· DVIDIA

Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured

Source & history · Download Markdown

Abstract

A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.

In this paper

DVIDIA research · 11 October 2026 · Compiled reference assembly and unloaded CPU diagnostics; physical transfer unmeasured

+

Pinned mechanics implementation and CPU numerical benchmark

Hardware matched robot simulation

· DVIDIA

Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured

Source & history · Download Markdown

Abstract

A separate UR5e/Robotiq reference preserves pinned geometry, inertias, moving-link collision proxies and coupled gripper structure: fourteen modeled coordinates and seven commands. Eighteen deterministic unloaded motion/closure runs are numerically valid; at a 1 ms step, CPU physics throughput measures 40.80–47.29 simulated seconds per measured stepping wall second on one host, excluding loading, sensing, rendering, policy and real actuation. Adjacent timestep comparisons and a matched static overlap expose numerical sensitivity and collision participation. A pure-Python paired trace scorer measures joint, tool and jaw mismatch without automatic qualification. Left/right five-finger references are separately audited; LeRobot collection with an original DVIDIA policy is proposed. Serial calibration, physical response, shoe task learning and bimanual execution remain unmeasured. An offline 3D mesh replay now exposes the recorded arm/gripper poses without rerunning physics. PiPER and SO-101 now have pinned CPU profiles with 6/6 valid unloaded numerical attempts each and explicit native command units. PiPER also has a frozen authored rigid-block contact pilot: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3, with all nine attempts numerically valid. One of 9,000 computation windows exceeds 20 ms; hardware real-time response is unqualified. SO-101 object tasks, learned policies and physical qualification remain open. YAM, OpenArm, ARX X5 and PAROL6 remain upcoming.

In this paper

DVIDIA research · 11 October 2026 · Compiled reference assembly and unloaded CPU diagnostics; physical transfer unmeasured

Build the first simulator around a specific six-axis arm and gripper, then measure the physical response that the learner will encounter. DVIDIA's vision remains a Skillspace that supplies demonstrations, local training and an installable skill. Rendered images support visual learning, but matching a room's appearance and the arm's number of joints does not establish that a learned action will produce the same motion, grip or outcome on hardware. We have compiled a pinned UR5e and Robotiq 2F-85 reference and run eighteen unloaded CPU diagnostics; this does not select the user's eventual purchase or reproduce that robot's controller. Later, two arms with five-finger hands need a different command, sensing and contact contract. Imported structure, numerical characterization and held-out physical agreement remain separate evidence states. Useful speed is the cost of acquiring a qualified capability, measured alongside errors and failures.

Six arm joints do not specify the whole robot

DVIDIA's existing dvidia-authored-6dof-parallel-jaw-v0 environment is an authored rigid-box contact experiment. It has six arm hinges, two simulated sliding jaws and eight native position actuators, exposed through six joint targets and one total-gap target. Arm links, base and palm have collision masks disabled; the experiment therefore does not evaluate whole-arm avoidance or self-collision. Exact object state, contact counts and pad forces are native simulator observations, not measured camera or fingertip signals. Preserve this profile and its frozen results while adding a separately named hardware reference. (Current arm source)

diff --git a/docs/papers/image-goal-jev-v01/index.html b/docs/papers/image-goal-jev-v01/index.html new file mode 100644 index 0000000..6175487 --- /dev/null +++ b/docs/papers/image-goal-jev-v01/index.html @@ -0,0 +1,49 @@ + + +Image-goal verification: native benchmark and Jev protocol · DVIDIA Research +

Native benchmark · Jev evaluation pending

Image-goal verification: native benchmark and Jev protocol

· DVIDIA

Does it only look finished?

32 native fixtures · 4 identical-image pairs · 64 Jev trials not run

Inspect the image-goal benchmark ↗ · Source & history · Download Markdown

Study abstract and scope

Abstract

A frozen PiPER image-goal cohort crosses eight physical conditions with four layouts. Four moving/settled pairs have byte-identical images and different completion labels, exposing when one image cannot establish task completion. Native measurements verify 32 authored fixtures; a paired 64-request Jev protocol distinguishes RGB judgments from privileged simulator telemetry. The provider evaluation has not run because credentials were unavailable: accuracy, false-completion rates and service latency remain unmeasured. Every native state, image, frozen request hash and explicit unrun result is retained. No learned policy or hardware qualification is claimed.

What is ready, and what remains unmeasured

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We have built and validated 32 native PiPER scene fixtures for an image-goal experiment. Four pairs have byte-identical current images and different physical completion labels: a settled shoe and the same shoe with an assigned velocity. The images alone cannot distinguish those states. A useful decision layer must recognize when the available evidence is insufficient.

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Jev has not been run. A TypeSafe credential was unavailable for this execution. All 64 planned provider trials remain explicitly not_run, and model accuracy, false-completion rates, service latency and cost remain unmeasured. This publication contains the native benchmark, frozen Jev protocol and runnable evaluation tools. It reports no successful model evaluation or learned robot controller.

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Inspect all current and goal images · Frozen evaluation plan · Explicit provider status

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The visual goal and the physical goal

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The task is to leave the green shoe at a marked position and orientation, supported by the table and released by the robot. Completion requires every declared condition to hold at each native sample throughout the final 200 ms window.

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Physical requirementFrozen threshold
Position error in the table planeAt most 25 mm
Yaw errorAt most 15°
Linear speedAt most 0.01 m/s
Angular speedAt most 0.05 rad/s
Table support forceGreater than 0.01 N
Robot contact forceAt most 0.01 N
Final qualifying window100 consecutive samples, 2 ms apart
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This is a sampled simulation criterion, not a continuous-time or physical hardware certification. Native force, motion and pose measurements determine the physical labels; no image model participates in their construction.

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The PiPER profile uses its existing six arm joints and coupled parallel gripper. The object is a declared 30 g rigid proxy with half the packaged trainer's width. Narrowing the object permits contact with the unchanged gripper for this diagnostic. It does not improve the full-size shoe results in our previous graspability study, which remain unchanged.

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The model assets are pinned to MuJoCo Menagerie commit 0059d4335f8156206f63a35662313385f7ad6d74. Ninety files, totaling 34,562,302 bytes, were verified against the existing lock before generation. The PiPER asset's MIT notice is retained in the evidence bundle. Pinned upstream asset.

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Eight conditions, four layouts

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The cohort crosses eight authored conditions with four deterministic layouts and camera azimuths. These are correlated diagnostic fixtures, not 32 independent real-world tasks or an unseen-object generalization study.

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ConditionCasesPhysical completionExpected judgment from one RGB viewExpected judgment with native telemetry
Settled at the goal4CompleteUnknownComplete
Target missing4IncompleteIncompleteIncomplete
Orange shoe in place of green target4IncompleteIncompleteIncomplete
Wrong position4IncompleteIncompleteIncomplete
Wrong orientation4IncompleteIncompleteIncomplete
Robot still in contact4IncompleteUnknownIncomplete
Moving, with the settled pose and pixels4IncompleteUnknownIncomplete
Completed target under an opaque cover4CompleteUnknownComplete
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Each fixture starts from an authored state. The unreleased condition uses a native approach and gripper closure; it does not demonstrate lifting. The moving condition assigns 0.12 m/s velocity without advancing time, deliberately preserving the settled pose and image. This is a declared state intervention, not a measured physical impulse or a completed manipulation episode.

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The four moving/settled pairs are the central negative control. Their current images and goal references match exactly, while the native velocity and completion labels differ. A single-image evaluator cannot recover an unobserved velocity from those identical inputs. Returning unknown is the intended behavior for both RGB-only cases. All four pairs also have identical complete RGB request hashes in the frozen evaluation plan; their telemetry requests differ. The RGB inputs do not leak the distinction through filenames or case categories.

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An image of visibly closed fingers also cannot establish the 0.01 N force threshold. The RGB-only label for the unreleased condition is therefore unknown. For an absent target, wrong identity or clear pose violation, a visible failed requirement is sufficient to answer incomplete.

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The generated cohort contains 8 physically complete and 24 physically incomplete cases, with 8 absent-target cases counting both missing objects and orange decoys. RGB-only evaluation has 16 known incomplete verdicts and 16 intended abstentions. The telemetry condition has 8 complete and 24 incomplete expected verdicts.

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What we give Jev

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The experiment pins TypeSafe's experimental image-capable jev-1.14.0. Each scheduled request contains the task contract, current image, goal image and one of two input conditions. Three typed questions request target-presence probability, physical-completion probability and a complete, incomplete or unknown verdict. Presence is declared only at probability 0.8 or greater, absence at 0.2 or less; values between them abstain. These thresholds and prompts are frozen before service calls. Image contract, model versions.

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The two conditions are:

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  • RGB: current and goal images with the task contract.
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  • RGB plus telemetry: the same images and task, with privileged simulator measurements of target presence, pose error, speed, contact forces, observation count and extrema across the full native window.
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The second condition deliberately supplies information that the image cannot establish. It includes oracle object presence and pose errors; it is not a demonstrated camera-based estimator. Its outcome would measure whether a decision service uses those supplied observations correctly. It would not demonstrate that Jev inferred force, speed or object identity from pixels.

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Ground-truth completion, expected verdicts and case-category names are excluded from model inputs. The complete native-window extrema preserve the evidence needed to evaluate the dwell rule, without substituting a precomputed completion label. The plan records semantic request hashes for all 64 paired trials in a deterministic shuffled order.

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Measure errors without rewarding an outage

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The tools preserve every planned trial. There are no automatic retries or post-result changes to prompts, thresholds or case selection. An interrupted in-flight request remains marked for investigation; the public report refuses to export an unresolved run.

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MeasureMeaning
Observability-aware accuracyExpected complete, incomplete or unknown verdicts over the planned cohort
Known-verdict accuracyCorrect judgments on cases with a defined observable completion verdict
Justified abstentionunknown on cases whose inputs cannot establish completion
Physical false completionModel says complete while the independent physical reference is incomplete
Missing-object false presenceModel says present when the target is physically absent
Coverage and errorsExplicit decisions, abstentions, failed requests and unrun cases, reported separately
Probability errorCompletion-probability Brier score against physical labels, with its valid-response count
Latencyp50, p95, p99 and maximum wall time, with errors retained in the all-attempt distribution
CostKnown-usage input-cost estimate and a separate count of attempts with unknown cost
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False-completion counts retain all planned incomplete cases and also show the denominator of valid negative-case responses. This prevents a service outage from appearing to improve the model merely because fewer answers arrive. Selective physical accuracy is reported with its answered-only denominator and coverage; abstention is not counted as successful physical completion.

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Online timing starts with preloaded image bytes and includes request preparation, image upload, hosted response and parsing. It excludes fixture-file loading, the earlier rendering, a live camera, robot actuation and physical task duration. The client has a bounded socket-operation timeout; this is not a real-time deadline guarantee. Small-sample p99 values describe this diagnostic cohort and cannot establish production tail latency.

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Native measurements and current limits

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The final local generation took 6.30 wall seconds for the 32 fixtures, including native scene construction, state validation, RGB/segmentation rendering and export. This is a fixture-generation measurement, not Jev inference speed, raw physics throughput or robot reaction time.

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The native generator verifies each declared condition and the four byte-identical image pairs. Each physical record retains its model state, current measurement and native history. The complete case manifest also retains target visibility from MuJoCo geometry segmentation. This segmentation is ground-truth diagnostic evidence; no open-vocabulary detector or learned perception model was evaluated here.

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An independent native audit recomputes all 32 physical labels, the sampled dwell rule and full-window telemetry extrema from recorded numbers. It verifies 67 original files, with eight physically complete cases and 24 incomplete cases. The runnable verifier checks recorded evidence without calling Jev or running a new simulation. Its source hash is included in the audit; the reproduction guide explains how to extract the original fixture tree.

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The shoe remains rigid, friction and actuation remain simulated, and the camera is authored and calibrated inside the simulation. The cohort does not cover deformable footwear, unmodeled clutter, arbitrary goal viewpoints, unseen objects or real sensor failures. The fixed arm reaches and closes only to construct the unreleased fixture; no Jev answer enters the actuator path.

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Run and inspect the benchmark

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The native generator and evaluator are frozen at 19acf0734d63ecebc07140c8d84302a54baee4c4, based on the published graspability branch. The publication source adds the viewer, independent verifier and publication tools without changing that core. Original DVIDIA code and authored fixture records use MIT. No Jev model weights or third-party training code are redistributed.

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Current/goal viewer · Every physical label · Native protocol · Complete package checksums · Reproduction and licenses

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The original native inventory, viewer inventory and outer publication inventory remain separate. Frozen native files are copied without modification. Public provider records exclude raw service responses and credentials; no provider response exists for this unrun execution.

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Python 3.12+, the repository's .[arm] dependencies and the pinned PiPER assets are sufficient for local generation. The static viewer runs without a GPU, credential or server-side model. Hosted Jev evaluation additionally needs a TypeSafe account and network access. The source guide documents secure environment-variable or explicitly named Keychain credentials; tokens never belong in the public files.

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Jev output is evaluated here, not used for training or distillation. The model remains subject to TypeSafe's own terms. Service agreement.

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What this adds to Skillspace

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An installable skill needs a completion contract alongside its goal image. The record should preserve the image, object identity, expected pose, relevant contacts and the interval over which the state must hold. The decision layer can then distinguish a visible failure from a state that needs another observation.

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The immediate next measurement is the frozen Jev cohort once credentials are available. After that, the useful comparison is a local state-based verifier, a visual evaluator and the same evaluator with measured feedback. Physical skill execution remains a separate benchmark: choose a subgoal, move the arm under local limits, and verify the resulting state. This experiment prepares that boundary; it does not yet cross it.

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diff --git a/docs/papers/native-grasp-task-benchmark/index.html b/docs/papers/native-grasp-task-benchmark/index.html index d6e8c81..99996eb 100644 --- a/docs/papers/native-grasp-task-benchmark/index.html +++ b/docs/papers/native-grasp-task-benchmark/index.html @@ -1,7 +1,7 @@ Native grasp task benchmark · DVIDIA Research -

Native contact task implementation and authored baseline

Native grasp task benchmark

· DVIDIA

6/6 simulated placements · controls 0/12 · real-time and hardware unqualified

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Abstract

A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.

In this paper

11 October 2026 · Native simulation benchmark and next learning experiment

+

Native contact task implementation and authored baseline

Native grasp task benchmark

· DVIDIA

6/6 simulated placements · controls 0/12 · real-time and hardware unqualified

Source & history · Download Markdown

Abstract

A pinned six-axis arm and coupled gripper complete 6/6 frozen synthetic rigid-block placements, while matched recorded-arm/open-jaw and hold controls complete 0/12. Native contact must produce grasp, sustained lift, retained carry, release and settled placement with tool retreat. All 18 attempts and timing traces remain in the receipt; one placement takes 29.51 wall seconds for 20 simulated seconds and misses 251 of 1,000 computation windows, so real-time operation is not established. An offline 3D mesh replay exposes the recorded motion. The authored privileged-state baseline provides a benchmark for a future compact DVIDIA action policy, not a learned footage-to-action model, validated shoe simulation or physical robot qualification.

In this paper

11 October 2026 · Native simulation benchmark and next learning experiment

DVIDIA's native contact benchmark completed 6/6 grasp, carry and release placements across six frozen synthetic layouts; matched open-jaw replay and hold controls completed 0/12. All 18 attempts were numerically valid. The six-axis reference arm, coupled parallel-jaw gripper and freely moving rigid block provide an executable task for a future learned action policy. The first baseline is authored and uses privileged simulator state; no policy has learned this task from footage. CPU execution also showed substantial timing variation, including a 29.51-second attempt for 20 seconds of simulated motion, so the result establishes task behavior under these settings rather than real-time control. Complete receipts, unsuccessful controls and source identities accompany the replay. Shoe organization and physical robot qualification require their own evidence. (Frozen confirmation receipt)

Native contact makes the task measurable

The reference combines six UR5e joint targets and one Robotiq 2F-85 actuator command. Its eight gripper linkage joints move through the upstream coupled mechanism; the action interface is one native jaw target in 0–255, rather than eight independent finger commands or a calibrated force request. The task adds a free-joint block and a support table while preserving the pinned arm/gripper geometry, inertias, active collision proxies and linkage constraints. An authored position controller and gravity feedforward act through the bounded native servos. During the rollout, the object moves through dynamics and contact rather than a weld or runtime pose assignment. The openly licensed simulation assets do not imply open-source UR5e or Robotiq physical hardware. (Frozen task source, hardware reference)

diff --git a/docs/papers/offline-cable-training-environment/index.html b/docs/papers/offline-cable-training-environment/index.html index 39b38f9..0a60b53 100644 --- a/docs/papers/offline-cable-training-environment/index.html +++ b/docs/papers/offline-cable-training-environment/index.html @@ -1,7 +1,7 @@ Offline cable training environment · DVIDIA Research -

Product prototype and local qualification

Offline cable training environment

· DVIDIA

CPU simulation-only · Controller search and frozen evaluation executed

Source & history · Download Markdown

Abstract

The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.

In this paper

Product prototype and local qualification · 7 October 2026 · Native CPU simulation only

+

Product prototype and local qualification

Offline cable training environment

· DVIDIA

CPU simulation-only · Controller search and frozen evaluation executed

Source & history · Download Markdown

Abstract

The first runnable product loop combines an anchored cable environment, local controller-parameter search, frozen evaluation, offline replay and a manifest-backed skill pack. The selected controller and a fixed feedback baseline both completed 10/10 held-out simulated episodes; zero-force and release controls completed 0/10. A native macOS network-denial run completed 3/3 additional episodes. Ideal endpoint attachment and privileged state bound the result; physical rope calibration, gripping, sensors and GPU throughput remain untested.

In this paper

Product prototype and local qualification · 7 October 2026 · Native CPU simulation only

The first runnable product loop now connects an authored environment → local practice → a frozen controller → evaluation → an inspectable skill pack. CableEndReach-v0 bends an anchored cable using bounded forces at its terminal site. The selected controller completed 10/10 held-out simulated episodes, but a fixed feedback baseline also completed 10/10 and finished faster. The result establishes an executable research and packaging foundation, with explicit failure controls. It does not establish physical rope accuracy, shoe tying, realistic grasping or a performance advantage over existing simulators.

Runnable source and instructions · Open the recorded replay · Download the skill pack · Complete qualification

The task has an explicit operating envelope

diff --git a/docs/papers/physical-skill-research-agenda/index.html b/docs/papers/physical-skill-research-agenda/index.html index f37262c..8b6dc12 100644 --- a/docs/papers/physical-skill-research-agenda/index.html +++ b/docs/papers/physical-skill-research-agenda/index.html @@ -1,7 +1,7 @@ Turn physical footage into testable evidence · DVIDIA Research -

Research agenda

Turn physical footage into testable evidence

· DVIDIA

Four baseline protocols proposed

Source & history · Download Markdown

Abstract

A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct.

In this paper

DVIDIA should test the usefulness of its evidence before expanding collection volume. The immediate opportunity is to make demonstrations easier to inspect, compare and reproduce, then measure whether added structure or physical signals help a defined task. Sam Padilla's 5 October essay provides a strategic prompt; the technical sources support narrower, testable questions. The four baseline protocols proposed in this agenda have not run, and neither customer demand nor robot transfer has been established. Four contributor-sized studies cover capture quality, relations over time, provenance and physical grounding.

+

Research agenda

Turn physical footage into testable evidence

· DVIDIA

Four baseline protocols proposed

Source & history · Download Markdown

Abstract

A research agenda covering visible capture evidence, relations over time, evaluation provenance and physical grounding. Each question has a bounded protocol and a failure condition. The aim is to measure whether structure and physical signals improve a defined task before scaling collection. Strategic opinions, author-reported results and our untested proposals are kept distinct.

In this paper

DVIDIA should test the usefulness of its evidence before expanding collection volume. The immediate opportunity is to make demonstrations easier to inspect, compare and reproduce, then measure whether added structure or physical signals help a defined task. Sam Padilla's 5 October essay provides a strategic prompt; the technical sources support narrower, testable questions. The four baseline protocols proposed in this agenda have not run, and neither customer demand nor robot transfer has been established. Four contributor-sized studies cover capture quality, relations over time, provenance and physical grounding.

The essay motivates a hypothesis, not a market forecast

In Robotics Data: Bet Hard Or Get Out, Sam Padilla, 5 October 2026, argues for exceptional collection scale, distinctive physical information or synthetic generation. He warns that buyers can internalize embodiment-specific collection and points to opportunities beyond raw data supply. His conversations and volume anecdote are not independently verified demand evidence. (Original essay).

Our inference is a practical test: does an attributable demonstration with observable state changes improve a specific evaluation more than a clip with coarse tags? This would establish a useful property of a research artifact. It would not establish willingness to pay, a durable business model or an executable robot skill. The first deliverable should therefore be a small benchmark with a clear failure condition, rather than a volume target.

diff --git a/docs/papers/piper-contact-history-v03/index.html b/docs/papers/piper-contact-history-v03/index.html index b18c694..9ef551b 100644 --- a/docs/papers/piper-contact-history-v03/index.html +++ b/docs/papers/piper-contact-history-v03/index.html @@ -1,7 +1,7 @@ PiPER contact history v0.3 · DVIDIA Research -

Paired probe-only and contact-history experiment with complete confirmation receipt

PiPER contact history v0.3

· DVIDIA

History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified

Source & history · Download Markdown

Abstract

A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.

In this paper

DVIDIA research · 11 October 2026 · Contact history v0.3

+

Paired probe-only and contact-history experiment with complete confirmation receipt

PiPER contact history v0.3

· DVIDIA

History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified

Source & history · Download Markdown

Abstract

A frozen paired PiPER experiment retains all 84 numerically valid attempts across twelve synthetic rigid-block scenes. History completes 6/12 placements versus 4/12 for both original motion and the identical acquisition probe without carry monitoring; all 48 separately matched open-jaw and hold controls fail. The two added completions occur at nominal friction with 60 g blocks. Four tasks complete after five closed holds/resumes, but only four of five resume windows meet the independent low absolute TCP-speed criterion. All six supported acquisition retries fail. Native gains, forces and outcome rules remain unchanged; requested movement reduction is distinct from actual speed, and two slowdown windows include early holds. Seventy-five of 84,000 computation windows exceed 20 ms. Complete sealed trajectories, original model notices, source-snapshotted development and pinned offline inspection preserve both success and failure. Privileged authored simulation establishes no learned skill, true-slip detector accuracy, shoe competence or physical qualification.

In this paper

DVIDIA research · 11 October 2026 · Contact history v0.3

History completes 6/12 placements, versus 4/12 for both original motion and probe-only motion. The two additional completions are the 60 g, nominal-friction cases; all three modes complete both 40 g moderate-friction cases and fail the 60 g moderate-friction and all low-friction cases. All 84 planned attempts remain numerically valid, including 48 unsuccessful negative controls. Four history tasks resume after a closed hold and complete, but all six supported acquisition retries still fail. This is a descriptive result on synthetic rigid blocks with privileged state, not detector accuracy, a learned skill or physical robot qualification. (Complete original report)

The question left by the previous guard

The earlier grip experiment diff --git a/docs/papers/piper-contact-recovery-v04/index.html b/docs/papers/piper-contact-recovery-v04/index.html index 73d7bd6..da28a7f 100644 --- a/docs/papers/piper-contact-recovery-v04/index.html +++ b/docs/papers/piper-contact-recovery-v04/index.html @@ -1,7 +1,7 @@ PiPER contact recovery v0.4 · DVIDIA Research -

Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt

PiPER contact recovery v0.4

· DVIDIA

All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified

Source & history · Download Markdown

Abstract

A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.

In this paper

DVIDIA research · 11 October 2026 · Contact recovery v0.4

+

Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt

PiPER contact recovery v0.4

· DVIDIA

All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified

Source & history · Download Markdown

Abstract

A fresh frozen 84-attempt PiPER comparison completes 4/12 placements for v0.3 history, v0.4 carry-only and v0.4 full; all 48 matched open-jaw and hold controls fail. Two completed post-hold placements finish 2.440 and 2.640 simulated seconds sooner with unchanged 220/240 ms settling holds. Four low-friction failures reach refusal 2.88–2.90 seconds sooner without becoming successes. Fresh nominal-friction 60 g layouts expose two held-but-too-low resume failures in every mode; neither reaches speed restoration. Six earlier acquisition failures are traced to insufficient support under the pinned position servo and side pinch, not the actuator force cap. All native mechanics and outcome criteria remain unchanged. Complete trajectories and 84,000 raw computation windows are retained; none exceeds 20 ms on this host. Source-snapshotted development remains separate. Privileged authored simulation establishes no learned skill, true-slip accuracy or physical qualification.

In this paper

DVIDIA research · 11 October 2026 · Contact recovery v0.4

v0.4 completes the same 4/12 placements as v0.3 on fresh layouts, while two completed recoveries finish 2.440 and 2.640 simulated seconds sooner. The carry-only and full versions have the same completion count; none is gained or lost. Four low-friction failures reach refusal 2.88–2.90 seconds sooner. All 84 planned attempts are numerically valid, including 48 failed negative controls. Eight placements still fail in each mode. Two nominal-friction 60 g cases that succeeded in the older cohort now remain suspended below the unchanged resume-height gate and time out in every mode. This establishes faster progress after settling on two tasks, with continuing acquisition and resume limitations. It is not a learned skill, true-slip accuracy or physical robot qualification. (Complete frozen report)

The six failed placements in contact history v0.3 all fail before acquiring a load-bearing grasp. A faster carry response cannot repair a grip that never lifts its object. Version 0.4 therefore separates two changes: restoring requested movement after a settled carry hold, and avoiding an unchanged supported retry when current observations indicate that it is not helping. Completed placement, successful recovery and faster refusal remain separate results.

Diagnose the six failures first

diff --git a/docs/papers/piper-grip-validation-and-recovery/index.html b/docs/papers/piper-grip-validation-and-recovery/index.html index f7c81ae..9f98991 100644 --- a/docs/papers/piper-grip-validation-and-recovery/index.html +++ b/docs/papers/piper-grip-validation-and-recovery/index.html @@ -1,7 +1,7 @@ PiPER grip validation and recovery · DVIDIA Research -

Paired authored controller experiment and complete confirmation receipt

PiPER grip validation and recovery

· DVIDIA

Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified

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Abstract

A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.

In this paper

DVIDIA research · 11 October 2026 · Authored native simulation experiment

+

Paired authored controller experiment and complete confirmation receipt

PiPER grip validation and recovery

· DVIDIA

Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified

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Abstract

A slow test lift and bounded supported retry add recorded acquisition decisions to the pinned PiPER contact pilot, but completion regresses: at moderate friction baseline completes 2/2 and guarded completes 0/2. Both complete 2/2 normal cases; neither completes the two low-friction cases. All 30 attempts remain numerically valid, including 18 failed controls and full-duration aborts. Four qualification decisions are followed by a brief independent following window, yet two tasks still fail; no successful recovery or overall improvement is demonstrated. Actual motion continues after hold requests, and 53/30,000 computation windows exceed 20 ms. Complete compressed receipts, original model notices and pinned offline inspection preserve the evidence. Authored privileged-state simulation establishes no detector accuracy, learned shoe skill, calibrated contact or physical robot qualification.

In this paper

DVIDIA research · 11 October 2026 · Authored native simulation experiment

The guarded controller regresses completion at moderate friction: baseline 2/2, guarded 0/2. Both complete 2/2 normal-friction placements and neither completes the two low-friction cases. Across the six paired scenes, placement completion falls from baseline 4/6 to guarded 2/6; all 18 open-jaw/hold controls fail and all 30 attempts remain numerically valid. The new controller exposes acquisition decisions, alarms, one supported retry and refusal, but demonstrates neither a successful recovery nor an overall completion improvement. These are synthetic rigid-block results, not a learned skill or physical robot qualification. (Complete original report)

A pinch is not yet a load-bearing grasp

The earlier PiPER contact pilot completed both nominal placements but failed to lift its slippery block. Both fingers registered contact while the tool moved away from the table and the block stayed supported. The missing decision was whether the object followed the gripper under load. This experiment adds a bounded test lift before the full carry. It preserves the pinned model, native servo settings, force limits, free-body object and independent task scorer. (Earlier pilot)

diff --git a/docs/papers/piper-reliability-v05/index.html b/docs/papers/piper-reliability-v05/index.html index 600077c..db3bc57 100644 --- a/docs/papers/piper-reliability-v05/index.html +++ b/docs/papers/piper-reliability-v05/index.html @@ -1,7 +1,7 @@ PiPER placement reliability v0.5 · DVIDIA Research -

Frozen paired reliability experiment with complete native evidence

PiPER placement reliability v0.5

· DVIDIA

23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified

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Abstract

Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.

In this paper

DVIDIA research · 12 October 2026 · Placement reliability v0.5

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Frozen paired reliability experiment with complete native evidence

PiPER placement reliability v0.5

· DVIDIA

23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified

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Abstract

Three frozen fresh PiPER cohorts retain 252 numerically valid attempts and 144 failed negative controls. A fixed diagonal grasp with bounded pose planning completes 23/36 placements versus 15/36 for frozen v0.4 and 18/36 with second-lift recovery alone: eight gained completions and none lost. The fifteen shared successes are 0.772 simulated seconds slower on average. Three lift-only second probes qualify and finish their tasks; the full grasp candidate needs none. Twelve low-friction acquisitions and one later retention failure remain. Every trajectory, native task outcome and raw computation duration is retained, including 1,035 windows exceeding 20 ms and a 3.082-second outlier on a loaded host. Native mechanics and scoring remain unchanged. Separate development keeps 200 earlier attempts, including discarded regressions. Authored privileged simulation establishes no learned skill, real-time guarantee or physical qualification.

In this paper

DVIDIA research · 12 October 2026 · Placement reliability v0.5

The revised grasp completes 23/36 placements, compared with 15/36 for frozen v0.4 and 18/36 with the second lift alone. Across three fresh cohorts, it gains eight completions and loses none, reducing failed placements from 21 to 13 in this narrow synthetic comparison. All 252 attempts are numerically valid; all 144 negative controls fail, with no solver warnings or prohibited native contact ticks. The fifteen shared successes take 0.772 simulated seconds longer on average with the revised grasp. Twelve low-friction acquisitions and one carry-retention failure remain. This is a measured completion improvement with a task-speed tradeoff, not learned competence or physical qualification. (Complete aggregate evidence)

The previous v0.4 recovery experiment made two recovered tasks finish sooner but did not complete more placements. Its failures exposed two different problems: a held object could settle below the original resume-height gate, or a supported object could never follow the initial pinch. Faster refusal did not solve either placement. Version 0.5 tests a separate response to each mechanism and measures the entire task again.

Two changes, one unchanged physical task

diff --git a/docs/papers/robotics-research-pipeline/index.html b/docs/papers/robotics-research-pipeline/index.html index 3188f86..35a5513 100644 --- a/docs/papers/robotics-research-pipeline/index.html +++ b/docs/papers/robotics-research-pipeline/index.html @@ -1,7 +1,7 @@ Robotics research pipeline · DVIDIA Research -

Architecture proposal and staged validation roadmap

Robotics research pipeline

· DVIDIA

Architecture proposal · current simulation alpha · hardware unqualified

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Abstract

An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.

In this paper

DVIDIA's product goal is a local training environment that converts Skillspace evidence into a versioned skill a compatible robot can install, test and execute with feedback. The next architectural step couples reviewed task descriptions to measured robot state, allowing a skill to decide whether a grasp worked, whether progress stopped and whether to retry or abstain. This paper specifies that pipeline and its validation gates. It adds no trained supervisor, human-video action bridge or hardware qualification. The implemented foundation remains a CPU research alpha with bounded simulation evidence. (Public training package, Published measurements)

+

Architecture proposal and staged validation roadmap

Robotics research pipeline

· DVIDIA

Architecture proposal · current simulation alpha · hardware unqualified

Source & history · Download Markdown

Abstract

An updated architecture connects immutable Skillspace recordings, reviewed task-label proposals and synchronized measured telemetry to grouped datasets, separate visual and motor learners, and a proposed local state supervisor. The implemented CPU baseline retains authored phases, gripper decisions and recovery around its learned joint-delta head. New annotation and supervisor pilots, fresh frozen confirmation, physics calibration and compatible skill-installation gates define the next milestones. Automatic labeling, learned supervision, human-video action recovery and physical arm qualification remain unestablished; this publication adds no new training or hardware result.

In this paper

DVIDIA's product goal is a local training environment that converts Skillspace evidence into a versioned skill a compatible robot can install, test and execute with feedback. The next architectural step couples reviewed task descriptions to measured robot state, allowing a skill to decide whether a grasp worked, whether progress stopped and whether to retry or abstain. This paper specifies that pipeline and its validation gates. It adds no trained supervisor, human-video action bridge or hardware qualification. The implemented foundation remains a CPU research alpha with bounded simulation evidence. (Public training package, Published measurements)

Pipeline at a glance

DVIDIA robotics pipeline: source evidence branches into reviewed label proposals and synchronized numerical telemetry; grouped datasets feed separate visual and motor learners, a proposed state supervisor, frozen simulation evaluation and compatible skill installation. Runtime observations return to bounded decisions and future reviewed recordings.
DVIDIA robotics pipeline: source evidence branches into reviewed label proposals and synchronized numerical telemetry; grouped datasets feed separate visual and motor learners, a proposed state supervisor, frozen simulation evaluation and compatible skill installation. Runtime observations return to bounded decisions and future reviewed recordings.

Figure 1. Solid elements represent implemented components within the bounded simulation pipeline; dashed elements represent proposed integrations. Physical robot execution remains unqualified. A visual model, reviewed label and executable motor policy have different evidence requirements. The diagram describes a target architecture, not a newly completed end-to-end system.

diff --git a/docs/papers/shoe-asset-library-v01/index.html b/docs/papers/shoe-asset-library-v01/index.html index c498301..cd157a3 100644 --- a/docs/papers/shoe-asset-library-v01/index.html +++ b/docs/papers/shoe-asset-library-v01/index.html @@ -1,7 +1,7 @@ Reusable shoe asset library v0.1 · DVIDIA Research -

Versioned native scene assets and frozen passive-contact benchmark

Reusable shoe asset library v0.1

· DVIDIA

Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified

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Abstract

Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.

In this paper

DVIDIA research · 12 October 2026 · Asset library v0.1

+

Versioned native scene assets and frozen passive-contact benchmark

Reusable shoe asset library v0.1

· DVIDIA

Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified

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Abstract

Six original MIT rigid shoe proxies compose with the pinned UR5e/Robotiq, PiPER and SO-101 models through versioned scene descriptors and offline native 3D previews. A frozen 54-attempt test records 27/27 supported pairs and 0/27 support successes when table collisions are disabled; all 54 gravity checks pass without numerical warnings. No loaded non-table shoe contacts appear in the 20 ms samples, which cannot rule out shorter transients. All sampled trajectories and 27,000 native-step durations remain available. On one Apple M5 host, 54 simulated seconds take 12.3616 wall seconds including fresh model construction; the slowest attempt takes 6.7047 seconds. Geometry and physical properties are authored assumptions. Loading, gravity and passive support do not establish shoe grasping, calibrated deformation, a learned policy or hardware transfer.

In this paper

DVIDIA research · 12 October 2026 · Asset library v0.1

The first DVIDIA asset library loads six original rigid shoe proxies into native simulation with UR5e/Robotiq, PiPER or SO-101. A versioned scene file records the exact catalog, robot, shoe identities and timestep. Users can browse the native geometry in 3D, download a selected pair's scene descriptor, and reproduce it locally using dvidia-assets. This release supplies reusable scene components and passive contact tests. A shoe-organizing policy is a subsequent task.

Explore the asset library · Hugging Face lab · Every benchmark attempt

What a shoe asset contains

diff --git a/docs/papers/skill-capsule-acquisition/index.html b/docs/papers/skill-capsule-acquisition/index.html index ee34685..b18c07e 100644 --- a/docs/papers/skill-capsule-acquisition/index.html +++ b/docs/papers/skill-capsule-acquisition/index.html @@ -1,7 +1,7 @@ Skill capsule acquisition and optimization · DVIDIA Research -

Learned movement artifact and installation prototype

Skill capsule acquisition and optimization

· DVIDIA

Simulation-only · teacher/student 7/8 · no overall speedup

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Abstract

A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.

In this paper

Measured prototype and proposed next steps · 7 October 2026 · Simulation-only

+

Learned movement artifact and installation prototype

Skill capsule acquisition and optimization

· DVIDIA

Simulation-only · teacher/student 7/8 · no overall speedup

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Abstract

A compact learned CPU movement head replaces joint-target computation inside an authored placement supervisor. Teacher and student both pass 7/8 in a new frozen evaluation, while episode wall time increases from 6.273 to 6.360 seconds. A 39,845-byte JSON capsule imports into the local lab, checks its exact runtime and qualifies the current scene through native placement and a recorded-arm/open-jaw control. Scene changes require a fresh test. One known scene and repeat pass native network denial; perception, complete task learning, other embodiments and hardware remain unqualified.

In this paper

Measured prototype and proposed next steps · 7 October 2026 · Simulation-only

DVIDIA now has a compact learned movement artifact that can be packaged for a compatible simulated arm. A normalized RBF head replaces the teacher's joint-target computation; task phases, waypoints, gripper/contact supervision, and bounded recovery remain authored. The frozen head contains 34,171 canonical JSON bytes, and the compiled candidate capsule contains 39,845 bytes. In a new eight-scene evaluation, teacher and student both completed 7/8, without teacher fallback, while total episode wall time increased from 6.273 to 6.360 seconds. This establishes a bounded teacher-to-student acquisition step, with neither reliability improvement nor overall acceleration demonstrated. The useful product path is import → compatibility check → actual local execution and negative control → scene-specific qualification → execution. Human-video learning, calibrated perception/contact, and physical-arm competence remain separate future capabilities. (Training receipt, profiling, candidate capsule).

The acquired behavior is movement inside an authored task

The experiment declares 16 training scenes, four separate development-selection scenes, and eight evaluation scenes with disjoint scene digests. Historical v0.3 held-out cases were excluded and their earlier results remain unchanged. The teacher completed 16/16 collection scenes, generating 6,344 supervised rows: 3,172 sampled native teacher calls and 3,172 extra teacher queries with perturbed Cartesian goals at the same physical states. Augmentation adds movement labels, not another 3,172 physical attempts. The full canonical dataset occupies 3,814,171 bytes. (Protocol, collection).

diff --git a/docs/papers/skillspace-arm-benchmarks/index.html b/docs/papers/skillspace-arm-benchmarks/index.html index f9544ac..1511ec6 100644 --- a/docs/papers/skillspace-arm-benchmarks/index.html +++ b/docs/papers/skillspace-arm-benchmarks/index.html @@ -1,7 +1,7 @@ Skillspace arm benchmarks and evolution · DVIDIA Research -

Paired simulation benchmark and product assessment

Skillspace arm benchmarks and evolution

· DVIDIA

Mixed held-out result · 30/32 baseline, 29/32 updated

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Abstract

Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.

In this paper

Research and implementation assessment · 7 October 2026 · Simulation-only evidence

+

Paired simulation benchmark and product assessment

Skillspace arm benchmarks and evolution

· DVIDIA

Mixed held-out result · 30/32 baseline, 29/32 updated

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Abstract

Two controller revisions improve development completion from 14/20 to 17/20, but the frozen paired test declines from 30/32 to 29/32: both pass all 24 normal-strength cases while actuator-stress completion falls from 6/8 to 5/8. Instrumented CPU throughput also declines. We publish the regression, unchanged contact and success rules, exact controller snapshots, richer physical inputs, runtime-bound installation and a proposed staged Skillspace contract. Contact forces and penetration remain uncalibrated; learned perception and physical qualification are open.

In this paper

Research and implementation assessment · 7 October 2026 · Simulation-only evidence

Skillspace → install → simulated arm makes sense as a grounded, compatible execution workflow. The present implementation imports task metadata, binds an explicitly authored adapter, supplies a metric scene, executes native contact dynamics, and checks completion. Two engineering rounds increased development success from 14/20 to 16/20 to 17/20, but the frozen held-out comparison declined from 30/32 to 29/32: both controllers passed all 24 nominal cases, while actuator-stress success fell from 6/8 to 5/8. There were zero held-out improvements and one regression. Observed instrumented CPU throughput also declined from 12.283× to 11.388× simulated seconds per wall second. The result establishes an auditable simulation workflow and useful failure diagnostics; it establishes neither improved held-out reliability nor calibrated physical accuracy. Skillspaces should evolve toward immutable releases that bind task, embodiment, observations, controller, and independently evaluated evidence. (Frozen assessment).

Installation supplies an explicit execution bridge

The public place_cup envelope contains a task label, robot identifier, recording format, and demonstration count. It does not contain learned weights, actions, object dimensions, metric target coordinates, or camera calibration. The local bridge supplies a declared rigid-box proxy and contact-pick-place-box-v0 adapter for dvidia-authored-6dof-parallel-jaw-v0. Thus installation selects existing authored behavior; it does not infer cup manipulation from the referenced demonstrations. This distinction preserves the useful architecture: language identifies the goal, grounding supplies the state and physical assumptions, a compatible controller acts, and a verifier judges the result. (Public metadata, execution guide).

diff --git a/docs/papers/skillspace-arm-installation/index.html b/docs/papers/skillspace-arm-installation/index.html index 77b3bbe..2e169d1 100644 --- a/docs/papers/skillspace-arm-installation/index.html +++ b/docs/papers/skillspace-arm-installation/index.html @@ -1,7 +1,7 @@ Installing a Skillspace on a simulated arm · DVIDIA Research -

Product prototype and native contact qualification

Installing a Skillspace on a simulated arm

· DVIDIA

Public skill link to authored adapter · 6/6 scene placements

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Abstract

The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.

In this paper

Research note, 7 October 2026. The current bridge makes a narrow part of the proposed skill workflow executable: import a DVIDIA skill envelope, install an explicitly authored local simulation adapter, create a scene, and run an articulated arm. It provides a concrete place to investigate observation-to-simulation-to-skill packaging. It does not yet infer an executable policy, object geometry, material properties, or robot calibration from human video.

+

Product prototype and native contact qualification

Installing a Skillspace on a simulated arm

· DVIDIA

Public skill link to authored adapter · 6/6 scene placements

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Abstract

The first link-to-arm product path installs an existing DVIDIA placement envelope, binds a local authored controller to one simulated arm, and reuses it in independently grounded scenes. Native jaw contacts carry a free box; six layouts complete, while no-motion and identical joint commands with open jaws each complete zero of six. Installed execution passes process-level offline checks. Task metadata contains no learned policy; perception, cup geometry, full arm collisions and physical qualification remain open.

In this paper

Research note, 7 October 2026. The current bridge makes a narrow part of the proposed skill workflow executable: import a DVIDIA skill envelope, install an explicitly authored local simulation adapter, create a scene, and run an articulated arm. It provides a concrete place to investigate observation-to-simulation-to-skill packaging. It does not yet infer an executable policy, object geometry, material properties, or robot calibration from human video.

The public example is DVIDIA place_cup, whose metadata is served at place_cup/skill.json. The recorded 360-byte source is vdia:0, version 0.3.0, with task “Set a cup on the mark.” Its exact-byte SHA-256 is 16b4e1ef0f3799345c1d18ce9f1da83b072863ab00ce30a83424080fb467d4ec. The envelope declares a demonstration count, recording format and robot identifier; these declarations do not supply trajectories, observations, trained weights, or an evaluated controller. The installation receipt preserves the source bytes, URL, source hash, separate grounding hash, adapter identity and physical_robot_ready: false. A later download may differ; its receipt must identify its own bytes.

In the local studio, one click installs the compatible public envelope and binds contact-pick-place-box-v0 to dvidia-authored-6dof-parallel-jaw-v0. A bundled envelope supports installation without fetching the public site. JSON upload/drop accepts the supported DVIDIA formats. Missing grounding is supplied through a visible, locally authored adapter; inline grounding remains separately validated. Installation downloads metadata and executes the already installed local controller. Object and target coordinates belong to the scene and can change between executions. The browser displays recorded native simulation poses rather than inventing an object trajectory. Execution and replay are designed to work with installed dependencies and local assets; the broader product's air-gapped deployment remains a separate validation task.

The embodiment is an original six-hinge arm with two actuated sliding jaws. Native MuJoCo 3.15.0 simulates a free rigid box, gravity and jaw/object/table contact. The default proxy is a 40 mm cube weighing 40 g on a table at 0.29 m. No weld, mocap attachment, or object-state update carries the object during execution. Arm controls use bounded position servos, model-based bias compensation and current-state damped Jacobian control. The declared limits are 40 N·m per arm actuator and 15 N per jaw. Physics uses a 1 ms timestep, implicitfast integration, Newton solving and an elliptic Coulomb friction cone. These choices describe an authored fixture, not measured hardware or calibrated material fidelity.

diff --git a/docs/papers/skillspace-footage-training/index.html b/docs/papers/skillspace-footage-training/index.html index d4f40c2..25bf238 100644 --- a/docs/papers/skillspace-footage-training/index.html +++ b/docs/papers/skillspace-footage-training/index.html @@ -1,7 +1,7 @@ Skillspace footage training pipeline · DVIDIA Research -

Product implementation and measured synthetic pilot

Skillspace footage training pipeline

· DVIDIA

Public CPU package · one native scene qualified · simulation-only

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Abstract

The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.

In this paper

Research pipeline evolution — 9 October 2026. The Robotics research pipeline adds the proposed annotation-review and measured-state task-supervisor layers around this implemented training path. It includes an architecture diagram, data contracts and separate validation gates. Those additions are proposals; the recorded pilots and frozen sample-count results below retain their original scope.

+

Product implementation and measured synthetic pilot

Skillspace footage training pipeline

· DVIDIA

Public CPU package · one native scene qualified · simulation-only

Source & history · Download Markdown

Abstract

The public Dvidia-Inference/dvidia-training repository provides a standalone CPU pipeline that audits Skillspace media, freezes grouped splits and exports inspectable artifacts. Ten synthetic six-second clips train a visual predictor with a 0.5106-second median pipeline time across three repeats on one macOS host. Aligned native simulator telemetry supplies 4,029 training labels for a learned joint-delta head, evaluated on one held-out recording. Its capsule passes native placement qualification in that recording's exact scene; identical joint targets with open jaws fail. Installation and hardware guides accompany the package. Human-video action recovery, shoe organization, demonstration sufficiency, independent scene generalization and physical robot competence remain unestablished.

In this paper

Research pipeline evolution — 9 October 2026. The Robotics research pipeline adds the proposed annotation-review and measured-state task-supervisor layers around this implemented training path. It includes an architecture diagram, data contracts and separate validation gates. Those additions are proposals; the recorded pilots and frozen sample-count results below retain their original scope.

The public, standalone DVIDIA training repository provides a local CPU pipeline that turns actual Skillspace media into a fitted visual model and, when aligned numeric actions are supplied, a learned movement head. The useful advance is a reproducible path from source files through provenance checks, grouped splits, measured fitting and portable artifacts. Video prediction and robot movement remain separate claims. Ten synthetic six-second clips demonstrate visual fitting; simulator telemetry supplies the movement pilot's action labels. Its exported capsule installs and passes native qualification in one exact scene, while identical joint targets with open jaws fail. Neither experiment learns shoe organization or reconstructs robot actions from human footage. This report documents the implemented path and its limited measured evidence, rather than establishing a sufficient demonstration count or hardware competence. (Standalone source archive, Qualification receipt)

Published artifacts on Hugging Face

The DVIDIA organization on Hugging Face mirrors the measured pilot for inspection and local reproduction:

diff --git a/docs/papers/spatially-grounded-downloadable-robot-skills/index.html b/docs/papers/spatially-grounded-downloadable-robot-skills/index.html index dd28dc8..fb5a5da 100644 --- a/docs/papers/spatially-grounded-downloadable-robot-skills/index.html +++ b/docs/papers/spatially-grounded-downloadable-robot-skills/index.html @@ -1,7 +1,7 @@ Spatially grounded downloadable robot skills · DVIDIA Research -

Research synthesis and proposed experiments

Spatially grounded downloadable robot skills

· DVIDIA

Source review · DVIDIA experiments not run

Source & history · Download Markdown

Abstract

A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.

In this paper

Research synthesis and proposed experiments · 7 October 2026 · No DVIDIA experiment in this report has run

+

Research synthesis and proposed experiments

Spatially grounded downloadable robot skills

· DVIDIA

Source review · DVIDIA experiments not run

Source & history · Download Markdown

Abstract

A synthesis of learning from human demonstrations, language-conditioned spatial attention, simulation expansion and robot-specific skill delivery. Spatial priors can accelerate inspection but do not determine contact state or control. The proposed downloadable skill packages a controller, initiation and termination conditions, compatible sensing and hardware, recovery behavior and measured evidence. Separate experiments would test attention, data coverage, feedback and transfer.

In this paper

Research synthesis and proposed experiments · 7 October 2026 · No DVIDIA experiment in this report has run

My judgment is that the thesis identifies a useful architecture, but its strongest version needs a measurable operating boundary. Human demonstrations can teach what a task means, which state changes matter, and where to inspect; turning those observations into robot control needs an action and embodiment bridge. A command-conditioned spatial prior could accelerate target discovery, provided observation corrects it when a shoe is on a table or the person changes posture. Simulation can expand grounded experience, but generated volume cannot establish coverage of every material, contact, damaged object, or recovery. Fifty examples could adapt a substantial pretrained physical skill model; they are not an established threshold for acquiring all of shoe tying. The falsifiable research claim is: for a fixed pretrained model and robot, task-state annotations, spatially directed inspection, and targeted real/simulated recovery data improve held-out physical success per unit of collection cost, while preserving performance on unusual placements. A downloadable skill is the resulting tested controller, its compatibility and task contract, and evidence of where it works.

This synthesis combines three parallel primary-source research tracks with the existing DVIDIA research context. Grok 4.7 was inaccessible and was not used: the inspected browser was signed out, the exact model could not be verified, and no prompt or response exists. Recent 2026 results below include author-reported preprints; none was independently reproduced here. Architecture, experiments, budgets, and release criteria are proposals.

Demonstrations teach meaning before they supply motor control

diff --git a/docs/publications.json b/docs/publications.json index 90c5ba9..1fd3fd2 100644 --- a/docs/publications.json +++ b/docs/publications.json @@ -5,6 +5,20 @@ "follow": "https://x.com/iammrriver", "contributor": null, "publications": [ + { + "slug": "image-goal-jev-v01", + "title": "Image-goal verification: native benchmark and Jev protocol", + "source": "reports/Image goal Jev v01.md", + "date": "2026-10-18", + "type": "Native diagnostic benchmark and frozen hosted-model protocol", + "status": "32 native fixtures · 4 identical-image pairs · 64 Jev trials not run", + "topics": [ + "physical-grounding", + "evaluation-provenance", + "skill-relations" + ], + "abstract": "A frozen PiPER image-goal cohort crosses eight physical conditions with four layouts. Four moving/settled pairs have byte-identical images and different completion labels, exposing when one image cannot establish task completion. Native measurements verify 32 authored fixtures; a paired 64-request Jev protocol distinguishes RGB judgments from privileged simulator telemetry. The provider evaluation has not run because credentials were unavailable: accuracy, false-completion rates and service latency remain unmeasured. Every native state, image, frozen request hash and explicit unrun result is retained. No learned policy or hardware qualification is claimed." + }, { "slug": "goal-images-physical-feedback", "title": "A visual goal still needs physical feedback", diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 559ba8e..4b60010 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -1,2 +1,2 @@ -https://research.dvidia.org/2026-10-18https://research.dvidia.org/papers/goal-images-physical-feedback/2026-10-18https://research.dvidia.org/papers/shoe-graspability-v03/2026-10-14https://research.dvidia.org/papers/continuous-shoe-grip-v02/2026-10-13https://research.dvidia.org/papers/skill-prerequisites-v01/2026-10-13https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/2026-10-13https://research.dvidia.org/papers/shoe-asset-library-v01/2026-10-12https://research.dvidia.org/papers/piper-reliability-v05/2026-10-12https://research.dvidia.org/papers/piper-contact-recovery-v04/2026-10-11https://research.dvidia.org/papers/piper-contact-history-v03/2026-10-11https://research.dvidia.org/papers/piper-grip-validation-and-recovery/2026-10-11https://research.dvidia.org/papers/native-grasp-task-benchmark/2026-10-11https://research.dvidia.org/papers/hardware-matched-robot-simulation/2026-10-11https://research.dvidia.org/papers/dvidia-observation-lab/2026-10-11https://research.dvidia.org/papers/robotics-research-pipeline/2026-10-09https://research.dvidia.org/papers/skillspace-footage-training/2026-10-08https://research.dvidia.org/papers/skill-capsule-acquisition/2026-10-07https://research.dvidia.org/papers/skillspace-arm-benchmarks/2026-10-07https://research.dvidia.org/papers/skillspace-arm-installation/2026-10-07https://research.dvidia.org/papers/offline-cable-training-environment/2026-10-07https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/2026-10-07https://research.dvidia.org/papers/grok-robotics-research-on-x/2026-10-07https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/2026-10-07https://research.dvidia.org/papers/clef-and-grounded-video-observations/2026-10-06https://research.dvidia.org/papers/from-skillspace-to-robot-skill/2026-10-06https://research.dvidia.org/papers/physical-skill-research-agenda/2026-10-06https://research.dvidia.org/topics/capture-quality/2026-10-11https://research.dvidia.org/topics/skill-relations/2026-10-18https://research.dvidia.org/topics/evaluation-provenance/2026-10-18https://research.dvidia.org/topics/physical-grounding/2026-10-18 +https://research.dvidia.org/2026-10-18https://research.dvidia.org/papers/image-goal-jev-v01/2026-10-18https://research.dvidia.org/papers/goal-images-physical-feedback/2026-10-18https://research.dvidia.org/papers/shoe-graspability-v03/2026-10-14https://research.dvidia.org/papers/continuous-shoe-grip-v02/2026-10-13https://research.dvidia.org/papers/skill-prerequisites-v01/2026-10-13https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/2026-10-13https://research.dvidia.org/papers/shoe-asset-library-v01/2026-10-12https://research.dvidia.org/papers/piper-reliability-v05/2026-10-12https://research.dvidia.org/papers/piper-contact-recovery-v04/2026-10-11https://research.dvidia.org/papers/piper-contact-history-v03/2026-10-11https://research.dvidia.org/papers/piper-grip-validation-and-recovery/2026-10-11https://research.dvidia.org/papers/native-grasp-task-benchmark/2026-10-11https://research.dvidia.org/papers/hardware-matched-robot-simulation/2026-10-11https://research.dvidia.org/papers/dvidia-observation-lab/2026-10-11https://research.dvidia.org/papers/robotics-research-pipeline/2026-10-09https://research.dvidia.org/papers/skillspace-footage-training/2026-10-08https://research.dvidia.org/papers/skill-capsule-acquisition/2026-10-07https://research.dvidia.org/papers/skillspace-arm-benchmarks/2026-10-07https://research.dvidia.org/papers/skillspace-arm-installation/2026-10-07https://research.dvidia.org/papers/offline-cable-training-environment/2026-10-07https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/2026-10-07https://research.dvidia.org/papers/grok-robotics-research-on-x/2026-10-07https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/2026-10-07https://research.dvidia.org/papers/clef-and-grounded-video-observations/2026-10-06https://research.dvidia.org/papers/from-skillspace-to-robot-skill/2026-10-06https://research.dvidia.org/papers/physical-skill-research-agenda/2026-10-06https://research.dvidia.org/topics/capture-quality/2026-10-11https://research.dvidia.org/topics/skill-relations/2026-10-18https://research.dvidia.org/topics/evaluation-provenance/2026-10-18https://research.dvidia.org/topics/physical-grounding/2026-10-18 diff --git a/docs/topics/capture-quality/index.html b/docs/topics/capture-quality/index.html index 5e836e1..1d7779e 100644 --- a/docs/topics/capture-quality/index.html +++ b/docs/topics/capture-quality/index.html @@ -1,7 +1,7 @@ Capture quality · DVIDIA Research -

Research question

Capture quality

DVIDIA · Visible evidence

Protocols and progress are scoped below

Question

Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.

Publications in this area

Question: Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.

+

Research question

Capture quality

DVIDIA · Visible evidence

Protocols and progress are scoped below

Question

Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.

Publications in this area

Question: Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.

Status: scoping · Updated 6 October 2026 · Results: not run

Open a research question · Baseline protocol · Bibliography

Primary sources

diff --git a/docs/topics/evaluation-provenance/index.html b/docs/topics/evaluation-provenance/index.html index b392229..7caf6aa 100644 --- a/docs/topics/evaluation-provenance/index.html +++ b/docs/topics/evaluation-provenance/index.html @@ -1,7 +1,7 @@ Evaluation and provenance · DVIDIA Research -

Research question

Evaluation and provenance

DVIDIA · Reproducible evidence

Protocols and progress are scoped below

Question

Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.

Publications in this area

Question: Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.

+

Research question

Evaluation and provenance

DVIDIA · Reproducible evidence

Protocols and progress are scoped below

Question

Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.

Publications in this area

Question: Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.

Status: evaluating · Updated 6 October 2026 · Results: image feasibility run complete; reference accuracy and provenance baselines not run

Open a research question · Baseline protocol · Bibliography

Primary sources

diff --git a/docs/topics/physical-grounding/index.html b/docs/topics/physical-grounding/index.html index fea80cb..c78d8be 100644 --- a/docs/topics/physical-grounding/index.html +++ b/docs/topics/physical-grounding/index.html @@ -1,7 +1,7 @@ Physical grounding · DVIDIA Research -

Research question

Physical grounding

DVIDIA · Signals beyond appearance

Protocols and progress are scoped below

Question

Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.

Publications in this area

Live PiPER shoe workstation — 13 October 2026

+

Research question

Physical grounding

DVIDIA · Signals beyond appearance

Protocols and progress are scoped below

Question

Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.

Publications in this area

Live PiPER shoe workstation — 13 October 2026

Live PiPER shoe workstation v0.1 adds browser-local MuJoCo physics, a fixed-orientation target controller, native jaw commands and a free rigid trainer on a physical worktop. Users can move a diff --git a/docs/topics/skill-relations/index.html b/docs/topics/skill-relations/index.html index a1cb873..042f43d 100644 --- a/docs/topics/skill-relations/index.html +++ b/docs/topics/skill-relations/index.html @@ -1,7 +1,7 @@ Skill relations · DVIDIA Research -

Research question

Skill relations

DVIDIA · Events with evidence

Protocols and progress are scoped below

Question

Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.

Publications in this area

Question: Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.

+

Research question

Skill relations

DVIDIA · Events with evidence

Protocols and progress are scoped below

Question

Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.

Publications in this area

Question: Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.

Status: scoping · Updated 6 October 2026 · Results: not run

Open a research question · Baseline protocol · Bibliography

Primary sources

diff --git a/publications.json b/publications.json index 8fe53d4..023714f 100644 --- a/publications.json +++ b/publications.json @@ -5,6 +5,20 @@ "follow": "https://x.com/iammrriver", "contributor": null, "publications": [ + { + "slug": "image-goal-jev-v01", + "title": "Image-goal verification: native benchmark and Jev protocol", + "source": "reports/Image goal Jev v01.md", + "date": "2026-10-18", + "type": "Native diagnostic benchmark and frozen hosted-model protocol", + "status": "32 native fixtures \u00b7 4 identical-image pairs \u00b7 64 Jev trials not run", + "topics": [ + "physical-grounding", + "evaluation-provenance", + "skill-relations" + ], + "abstract": "A frozen PiPER image-goal cohort crosses eight physical conditions with four layouts. Four moving/settled pairs have byte-identical images and different completion labels, exposing when one image cannot establish task completion. Native measurements verify 32 authored fixtures; a paired 64-request Jev protocol distinguishes RGB judgments from privileged simulator telemetry. The provider evaluation has not run because credentials were unavailable: accuracy, false-completion rates and service latency remain unmeasured. Every native state, image, frozen request hash and explicit unrun result is retained. No learned policy or hardware qualification is claimed." + }, { "slug": "goal-images-physical-feedback", "title": "A visual goal still needs physical feedback", diff --git a/reports/Image goal Jev v01.md b/reports/Image goal Jev v01.md new file mode 100644 index 0000000..9004131 --- /dev/null +++ b/reports/Image goal Jev v01.md @@ -0,0 +1,113 @@ +# Image-goal verification: native benchmark and Jev protocol + +## What is ready, and what remains unmeasured + +We have built and validated **32 native PiPER scene fixtures** for an image-goal experiment. Four pairs have byte-identical current images and different physical completion labels: a settled shoe and the same shoe with an assigned velocity. The images alone cannot distinguish those states. A useful decision layer must recognize when the available evidence is insufficient. + +**Jev has not been run.** A TypeSafe credential was unavailable for this execution. All 64 planned provider trials remain explicitly `not_run`, and model accuracy, false-completion rates, service latency and cost remain unmeasured. This publication contains the native benchmark, frozen Jev protocol and runnable evaluation tools. It reports no successful model evaluation or learned robot controller. + +[Inspect all current and goal images](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html) · [Frozen evaluation plan](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json) · [Explicit provider status](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json) + +## The visual goal and the physical goal + +The task is to leave the green shoe at a marked position and orientation, supported by the table and released by the robot. Completion requires every declared condition to hold at each native sample throughout the final 200 ms window. + +| Physical requirement | Frozen threshold | +| --- | ---: | +| Position error in the table plane | At most 25 mm | +| Yaw error | At most 15° | +| Linear speed | At most 0.01 m/s | +| Angular speed | At most 0.05 rad/s | +| Table support force | Greater than 0.01 N | +| Robot contact force | At most 0.01 N | +| Final qualifying window | 100 consecutive samples, 2 ms apart | + +This is a sampled simulation criterion, not a continuous-time or physical hardware certification. Native force, motion and pose measurements determine the physical labels; no image model participates in their construction. + +The PiPER profile uses its existing six arm joints and coupled parallel gripper. The object is a declared **30 g rigid proxy with half the packaged trainer's width**. Narrowing the object permits contact with the unchanged gripper for this diagnostic. It does not improve the full-size shoe results in our [previous graspability study](Shoe%20graspability%20v03.md), which remain unchanged. + +The model assets are pinned to MuJoCo Menagerie commit `0059d4335f8156206f63a35662313385f7ad6d74`. Ninety files, totaling 34,562,302 bytes, were verified against the existing lock before generation. The PiPER asset's MIT notice is retained in the evidence bundle. [Pinned upstream asset](https://github.com/google-deepmind/mujoco_menagerie/tree/0059d4335f8156206f63a35662313385f7ad6d74/agilex_piper). + +## Eight conditions, four layouts + +The cohort crosses eight authored conditions with four deterministic layouts and camera azimuths. These are correlated diagnostic fixtures, not 32 independent real-world tasks or an unseen-object generalization study. + +| Condition | Cases | Physical completion | Expected judgment from one RGB view | Expected judgment with native telemetry | +| --- | ---: | --- | --- | --- | +| Settled at the goal | 4 | Complete | Unknown | Complete | +| Target missing | 4 | Incomplete | Incomplete | Incomplete | +| Orange shoe in place of green target | 4 | Incomplete | Incomplete | Incomplete | +| Wrong position | 4 | Incomplete | Incomplete | Incomplete | +| Wrong orientation | 4 | Incomplete | Incomplete | Incomplete | +| Robot still in contact | 4 | Incomplete | Unknown | Incomplete | +| Moving, with the settled pose and pixels | 4 | Incomplete | Unknown | Incomplete | +| Completed target under an opaque cover | 4 | Complete | Unknown | Complete | + +Each fixture starts from an authored state. The unreleased condition uses a native approach and gripper closure; it does not demonstrate lifting. The moving condition assigns 0.12 m/s velocity without advancing time, deliberately preserving the settled pose and image. This is a declared state intervention, not a measured physical impulse or a completed manipulation episode. + +The four moving/settled pairs are the central negative control. Their current images and goal references match exactly, while the native velocity and completion labels differ. A single-image evaluator cannot recover an unobserved velocity from those identical inputs. Returning `unknown` is the intended behavior for both RGB-only cases. All four pairs also have identical complete RGB request hashes in the frozen evaluation plan; their telemetry requests differ. The RGB inputs do not leak the distinction through filenames or case categories. + +An image of visibly closed fingers also cannot establish the 0.01 N force threshold. The RGB-only label for the unreleased condition is therefore `unknown`. For an absent target, wrong identity or clear pose violation, a visible failed requirement is sufficient to answer `incomplete`. + +The generated cohort contains **8 physically complete and 24 physically incomplete cases**, with **8 absent-target cases** counting both missing objects and orange decoys. RGB-only evaluation has 16 known incomplete verdicts and 16 intended abstentions. The telemetry condition has 8 complete and 24 incomplete expected verdicts. + +## What we give Jev + +The experiment pins TypeSafe's experimental image-capable `jev-1.14.0`. Each scheduled request contains the task contract, current image, goal image and one of two input conditions. Three typed questions request target-presence probability, physical-completion probability and a `complete`, `incomplete` or `unknown` verdict. Presence is declared only at probability 0.8 or greater, absence at 0.2 or less; values between them abstain. These thresholds and prompts are frozen before service calls. [Image contract](https://docs.typesafe.ai/images), [model versions](https://docs.typesafe.ai/models). + +The two conditions are: + +- **RGB:** current and goal images with the task contract. +- **RGB plus telemetry:** the same images and task, with privileged simulator measurements of target presence, pose error, speed, contact forces, observation count and extrema across the full native window. + +The second condition deliberately supplies information that the image cannot establish. It includes oracle object presence and pose errors; it is not a demonstrated camera-based estimator. Its outcome would measure whether a decision service uses those supplied observations correctly. It would not demonstrate that Jev inferred force, speed or object identity from pixels. + +Ground-truth completion, expected verdicts and case-category names are excluded from model inputs. The complete native-window extrema preserve the evidence needed to evaluate the dwell rule, without substituting a precomputed completion label. The plan records semantic request hashes for all 64 paired trials in a deterministic shuffled order. + +## Measure errors without rewarding an outage + +The tools preserve every planned trial. There are no automatic retries or post-result changes to prompts, thresholds or case selection. An interrupted in-flight request remains marked for investigation; the public report refuses to export an unresolved run. + +| Measure | Meaning | +| --- | --- | +| Observability-aware accuracy | Expected `complete`, `incomplete` or `unknown` verdicts over the planned cohort | +| Known-verdict accuracy | Correct judgments on cases with a defined observable completion verdict | +| Justified abstention | `unknown` on cases whose inputs cannot establish completion | +| Physical false completion | Model says complete while the independent physical reference is incomplete | +| Missing-object false presence | Model says present when the target is physically absent | +| Coverage and errors | Explicit decisions, abstentions, failed requests and unrun cases, reported separately | +| Probability error | Completion-probability Brier score against physical labels, with its valid-response count | +| Latency | p50, p95, p99 and maximum wall time, with errors retained in the all-attempt distribution | +| Cost | Known-usage input-cost estimate and a separate count of attempts with unknown cost | + +False-completion counts retain all planned incomplete cases and also show the denominator of valid negative-case responses. This prevents a service outage from appearing to improve the model merely because fewer answers arrive. Selective physical accuracy is reported with its answered-only denominator and coverage; abstention is not counted as successful physical completion. + +Online timing starts with preloaded image bytes and includes request preparation, image upload, hosted response and parsing. It excludes fixture-file loading, the earlier rendering, a live camera, robot actuation and physical task duration. The client has a bounded socket-operation timeout; this is not a real-time deadline guarantee. Small-sample p99 values describe this diagnostic cohort and cannot establish production tail latency. + +## Native measurements and current limits + +The final local generation took **6.30 wall seconds** for the 32 fixtures, including native scene construction, state validation, RGB/segmentation rendering and export. This is a fixture-generation measurement, not Jev inference speed, raw physics throughput or robot reaction time. + +The native generator verifies each declared condition and the four byte-identical image pairs. Each physical record retains its model state, current measurement and native history. The complete case manifest also retains target visibility from MuJoCo geometry segmentation. This segmentation is ground-truth diagnostic evidence; no open-vocabulary detector or learned perception model was evaluated here. + +An [independent native audit](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json) recomputes all 32 physical labels, the sampled dwell rule and full-window telemetry extrema from recorded numbers. It verifies 67 original files, with eight physically complete cases and 24 incomplete cases. The [runnable verifier](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py) checks recorded evidence without calling Jev or running a new simulation. Its source hash is included in the audit; the reproduction guide explains how to extract the original fixture tree. + +The shoe remains rigid, friction and actuation remain simulated, and the camera is authored and calibrated inside the simulation. The cohort does not cover deformable footwear, unmodeled clutter, arbitrary goal viewpoints, unseen objects or real sensor failures. The fixed arm reaches and closes only to construct the unreleased fixture; no Jev answer enters the actuator path. + +## Run and inspect the benchmark + +The [native generator and evaluator](https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4) are frozen at `19acf0734d63ecebc07140c8d84302a54baee4c4`, based on the published graspability branch. The [publication source](https://github.com/Dvidia-Inference/dvidia-training/tree/3d3c8f660f6f9544d6a58c654bab143dd1eddc00) adds the viewer, independent verifier and publication tools without changing that core. Original DVIDIA code and authored fixture records use MIT. No Jev model weights or third-party training code are redistributed. + +[Current/goal viewer](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html) · [Every physical label](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json) · [Native protocol](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json) · [Complete package checksums](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json) · [Reproduction and licenses](../topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md) + +The original native inventory, viewer inventory and outer publication inventory remain separate. Frozen native files are copied without modification. Public provider records exclude raw service responses and credentials; no provider response exists for this unrun execution. + +Python 3.12+, the repository's `.[arm]` dependencies and the pinned PiPER assets are sufficient for local generation. The static viewer runs without a GPU, credential or server-side model. Hosted Jev evaluation additionally needs a TypeSafe account and network access. The source guide documents secure environment-variable or explicitly named Keychain credentials; tokens never belong in the public files. + +Jev output is evaluated here, not used for training or distillation. The model remains subject to TypeSafe's own terms. [Service agreement](https://typesafe.ai/legal/mca). + +## What this adds to Skillspace + +An installable skill needs a completion contract alongside its goal image. The record should preserve the image, object identity, expected pose, relevant contacts and the interval over which the state must hold. The decision layer can then distinguish a visible failure from a state that needs another observation. + +The immediate next measurement is the frozen Jev cohort once credentials are available. After that, the useful comparison is a local state-based verifier, a visual evaluator and the same evaluator with measured feedback. Physical skill execution remains a separate benchmark: choose a subgoal, move the arm under local limits, and verify the resulting state. This experiment prepares that boundary; it does not yet cross it. diff --git a/scripts/build-papers.mjs b/scripts/build-papers.mjs index ba16c0c..273369d 100644 --- a/scripts/build-papers.mjs +++ b/scripts/build-papers.mjs @@ -12,6 +12,11 @@ const DOCS = resolve(ROOT, "docs"); const TOPICS = ["capture-quality", "skill-relations", "evaluation-provenance", "physical-grounding"]; const SLUG = /^[a-z0-9]+(?:-[a-z0-9]+)*$/; const READERS = { + "image-goal-jev-v01": { + eyebrow: "Native benchmark · Jev evaluation pending", deck: "Does it only look finished?", + entry: "downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html", + action: "Inspect the image-goal benchmark" + }, "goal-images-physical-feedback": { eyebrow: "Research direction · Native contact evidence", deck: "A destination to aim for. Feedback to get there.", entry: "downloads/topics/physical-grounding/runs/2026-10-18-native-contact-audit/audit/report.json", @@ -222,7 +227,7 @@ export async function buildPublications() { pages.push({ path: page, date: papers.reduce((latest, paper) => paper.date > latest ? paper.date : latest, topic.updated) }); } const paperRows = catalog.publications.map(p => `

· ${escapeHtml(p.type)}

${escapeHtml(p.title)}

${escapeHtml(p.abstract)}

${escapeHtml(p.status)}

`).join(""); - const content = `

An open research notebook

From observation
to physical skill.

We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.

Published by DVIDIA · hello@dvidia.org

Publications

${catalog.publications.length} papers and working notes

${paperRows}

Research footprints

  1. Connect visual goals to measured physical feedback

    Fourteen passive rigid-box arm replays reproduce every original sampled state and outcome, recording 249,502 native ticks. Millisecond contact gaps appear between stored observations; a successful comparator also has gaps. The separate architecture note evaluates Jev as a candidate decision layer and defines a goal-image reaching test. No visual model is integrated and no controller is promoted.

    Read the architecture and audit · Download verified evidence tool

  2. Separate object difficulty from the gripper and attached tool

    A frozen direct-grasp diagnostic retains 24 valid attempts. Lightweight controls complete 8/12 lifts; every 300 g condition fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result. Relative lift, settled references and dense contact traces make the modeled drive and geometry limits visible. These are diagnostic controls, not a shoe retrieval rate or hardware qualification.

    Read the graspability study · Inspect every recorded attempt

  3. Measure contact-loss response without claiming a better grasp

    Twenty valid fresh attempts complete the same 2/10 cases under both controllers. Nine trace-verified bilateral-contact-loss alarms arrive in 20–52 ms; twelve supported lowering responses produce no additional completion. Full trajectories and an independent audit expose failed acquisition and an unmonitored support-contamination gap. Hardware transfer remains open.

    Read the continuous-grip comparison · Inspect the recorded attempts

  4. Make skill prerequisites and failed recovery measurable

    Two native environments retain sixteen valid held-out attempts. A lid guard avoids two baseline contacts while completion remains 2/4. A mounted rake reaches three of four intermediate retrieval checkpoints, but no subsequent retained lift succeeds. Recorded replays expose every failure; fresh physics runs locally. Tool acquisition, learned control and hardware transfer remain open.

    Read the paired results · Explore both recorded experiments

  5. Move the PiPER arm in a live browser workstation

    Drag a target and nudge a free rigid trainer with live browser MuJoCo physics. The final native run keeps 20 valid attempts: 14/16 targets pass, both high-target repeats fail, and four mechanics checks pass. A ten-minute browser run reports 51.39 render calls per second on the tested host. Strict raw-contact parity fails in 13/1,500 sampled states despite numerical agreement; all fifteen development shoe-lift attempts fail. Learned shoe skills, calibrated contact and hardware transfer remain open.

    Read the workstation and evidence · Open the live workstation

  6. Load reusable shoes with three native robot arms

    Six original rigid shoe proxies now compose with UR5e/Robotiq, PiPER and SO-101. Versioned scene files, offline 3D previews and a frozen 54-attempt benchmark retain every trajectory: supported pairs 27/27, disabled-table support 0/27, all gravity checks pass. Native loading and passive contact are tested; shoe grasping, deformation and physical transfer remain unqualified.

    Read the asset library results · Browse shoes and arms

  7. Complete more placements with a bounded second lift and revised grasp

    Three fresh cohorts retain 252 valid attempts. The revised grasp completes 23/36 placements versus 15/36 for v0.4 and 18/36 with second lift alone; all 144 controls fail. Eight completions are gained and none lost, while shared successes average 0.772 simulated seconds slower. Twelve low-friction acquisitions and one retention failure remain. All 1,035 computation overruns are retained; physical qualification is open.

    Read the v0.5 reliability comparison · Inspect all three cohorts

  8. Separate faster recovery from an ineffective retry

    A fresh 84-attempt comparison completes 4/12 placements in every mode; all 48 controls fail. Two resumed tasks finish 2.440–2.640 simulated seconds sooner after unchanged settling holds, while four failed grips are refused 2.88–2.90 seconds sooner. Eight failures remain, including two held blocks below the resume-height gate. Every native trajectory and raw computation duration is retained; physical qualification is open.

    Read the v0.4 recovery comparison · Inspect every paired attempt

  9. Compare probe-only motion with contact history

    A frozen 84-attempt paired cohort completes 6/12 history placements versus 4/12 original and probe-only placements; all 48 controls fail. Four tasks complete after five closed holds/resumes, but all six supported acquisition retries fail. Only four of five resume windows meet the independent low absolute tool-speed check. Actual motion and 75 missed computation windows remain visible; physical robot qualification is open.

    Read the contact history comparison · Inspect all 84 attempts

  10. Test the grip and publish a completion regression

    A slow probe and one supported retry retain 30 valid attempts. At moderate friction baseline completes 2/2 while guarded completes 0/2; normal cases both pass and low-friction cases both fail. No successful recovery or overall improvement is demonstrated. Four brief following windows do not establish detector accuracy; 53 computation windows miss 20 ms. Every full attempt remains public.

    Read the paired results and actual stop motion · Inspect all 30 attempts

  11. Measure PiPER contact and a friction failure

    A frozen authored rigid-block pilot retains nine valid attempts: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3. One of 9,000 computation windows exceeds 20 ms. Complete task receipts remain distinct from unloaded profiles; learned skills, shoe physics and physical qualification are open.

    Read the PiPER contact pilot · Open the Robot Simulation Lab

  12. Run more arms on a local CPU

    PiPER and SO-101 join the library with pinned models, explicit arm/gripper units, preserved licenses and complete numerical receipts. Each completes six unloaded motion checks offline. Their learned-policy and physical qualification remain open. The PiPER contact pilot and UR5e grasp demonstration have separate task receipts.

    Read the profile measurements · Open the Robot Simulation Lab

  13. Give the reference arm a contact task

    A free rigid block must be grasped, lifted, carried, released and left settled at its destination. Frozen scenarios compare the authored controller with identical arm commands and open jaws, and with a hold control. Full traces and 3D replay preserve the evidence for the next compact action-learning experiment. Shoe physics, a learned task policy and hardware transfer remain open.

    Read the native task benchmark · Replay grasp and placement

  14. Build a hardware reference we can measure

    A pinned six-axis arm and coupled gripper expose fourteen coordinates and seven commands. All 18 unloaded numerical runs are valid; 1 ms CPU physics measures 40.80–47.29× elapsed-time throughput with sensing, rendering and policy excluded. A paired trace scorer makes physical mismatch measurable. Five-finger references and LeRobot collection have separate contracts; hardware fidelity and shoe competence remain unmeasured.

    Read the mechanical contract and upcoming arms · Open the 3D replay

  15. Learn a compact command-to-motion predictor

    A 6,438-byte CPU model predicts the next 20 ms of joint/jaw movement from native state and issued commands. Twelve seeded groups retain 36 recordings; 4,553 test transitions are not independent trials. Warm computation measures 0.207 ms p95, but jaw mean absolute/worst errors regress against persistence and the first opening is overpredicted. Shoe collection, hardware sensing and validated shoe physics remain open.

    Read the measured pilot and shoe collection plan

  16. Observe image motion and forecast its path

    A manually seeded CPU source alpha records pixel speed and causal forecasts. Six authored cases process 720 frames in 1.454 seconds; occlusion and low-texture losses remain visible, and a 500 ms reversal forecast misses by nearly 90 pixels. The v0.1 release/demo remain separate; real-scene accuracy, metric speed and live robot control are unqualified.

    Read the source alpha and measured limitations

  17. Publish the robotics research pipeline

    A diagram connects footage review, measured robot state, local training, frozen simulation evaluation and compatible skill installation. Automatic annotation and a learned task supervisor are proposed; their companion validation studies and physical robot qualification remain unrun.

    Read the architecture and validation gates

  18. A public skill link reaches a simulated arm

    One-click local installation binds a DVIDIA placement envelope to an authored contact controller. Six scene layouts pass; identical joint commands with open jaws and no-motion controls each pass 0/6. Physical transfer and learned policies remain open.

    Read the installation contract and arm replay

  19. The offline cable environment is runnable

    A local controller-training loop and simulation-only skill pack now run on CPU. Trained and baseline feedback each pass 10/10 simulated seeds; zero force and early release each pass 0/10. Real gripping, calibration and GPU qualification remain open.

    Read the first product build and replay

  20. Contact accuracy meets runtime

    54 deterministic CPU fixture trials recorded. Endpoint agreement coexists with unqualified force transients; coarse wall tests expose missed contact. No GPU or physical-transfer claim.

    Read the measurements

  21. Compare the observation-to-skill thesis with prior work

    Spatial attention, demonstration coverage, executable simulation and reusable repairs are reviewed separately. Three parallel Grok 4.7 searches support discovery; primary sources support the claims.

    Read the comparison

  22. Five licensed POV excerpts processed

    Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.

    Read the feasibility run

  23. Define the evidence and release questions

    Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.

    Read the agenda

Open questions

Follow new publications through RSS or inspect the source and revisions on GitHub.

`; + const content = `

An open research notebook

From observation
to physical skill.

We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.

Published by DVIDIA · hello@dvidia.org

Publications

${catalog.publications.length} papers and working notes

${paperRows}

Research footprints

  1. Make image-goal uncertainty testable

    Thirty-two native PiPER fixtures include four moving/settled pairs with identical images and different physical completion labels. The frozen 64-request Jev protocol compares RGB with privileged simulator telemetry. Jev has not been run; all provider trials remain explicitly unrun, with accuracy and service latency unmeasured. Native states, images, source hashes and physical labels are public.

    Read the benchmark and protocol · Compare every current and goal image

  2. Connect visual goals to measured physical feedback

    Fourteen passive rigid-box arm replays reproduce every original sampled state and outcome, recording 249,502 native ticks. Millisecond contact gaps appear between stored observations; a successful comparator also has gaps. The separate architecture note evaluates Jev as a candidate decision layer and defines a goal-image reaching test. No visual model is integrated and no controller is promoted.

    Read the architecture and audit · Download verified evidence tool

  3. Separate object difficulty from the gripper and attached tool

    A frozen direct-grasp diagnostic retains 24 valid attempts. Lightweight controls complete 8/12 lifts; every 300 g condition fails, including the small block. Removing the rake gains one narrow-shoe result and loses one block result. Relative lift, settled references and dense contact traces make the modeled drive and geometry limits visible. These are diagnostic controls, not a shoe retrieval rate or hardware qualification.

    Read the graspability study · Inspect every recorded attempt

  4. Measure contact-loss response without claiming a better grasp

    Twenty valid fresh attempts complete the same 2/10 cases under both controllers. Nine trace-verified bilateral-contact-loss alarms arrive in 20–52 ms; twelve supported lowering responses produce no additional completion. Full trajectories and an independent audit expose failed acquisition and an unmonitored support-contamination gap. Hardware transfer remains open.

    Read the continuous-grip comparison · Inspect the recorded attempts

  5. Make skill prerequisites and failed recovery measurable

    Two native environments retain sixteen valid held-out attempts. A lid guard avoids two baseline contacts while completion remains 2/4. A mounted rake reaches three of four intermediate retrieval checkpoints, but no subsequent retained lift succeeds. Recorded replays expose every failure; fresh physics runs locally. Tool acquisition, learned control and hardware transfer remain open.

    Read the paired results · Explore both recorded experiments

  6. Move the PiPER arm in a live browser workstation

    Drag a target and nudge a free rigid trainer with live browser MuJoCo physics. The final native run keeps 20 valid attempts: 14/16 targets pass, both high-target repeats fail, and four mechanics checks pass. A ten-minute browser run reports 51.39 render calls per second on the tested host. Strict raw-contact parity fails in 13/1,500 sampled states despite numerical agreement; all fifteen development shoe-lift attempts fail. Learned shoe skills, calibrated contact and hardware transfer remain open.

    Read the workstation and evidence · Open the live workstation

  7. Load reusable shoes with three native robot arms

    Six original rigid shoe proxies now compose with UR5e/Robotiq, PiPER and SO-101. Versioned scene files, offline 3D previews and a frozen 54-attempt benchmark retain every trajectory: supported pairs 27/27, disabled-table support 0/27, all gravity checks pass. Native loading and passive contact are tested; shoe grasping, deformation and physical transfer remain unqualified.

    Read the asset library results · Browse shoes and arms

  8. Complete more placements with a bounded second lift and revised grasp

    Three fresh cohorts retain 252 valid attempts. The revised grasp completes 23/36 placements versus 15/36 for v0.4 and 18/36 with second lift alone; all 144 controls fail. Eight completions are gained and none lost, while shared successes average 0.772 simulated seconds slower. Twelve low-friction acquisitions and one retention failure remain. All 1,035 computation overruns are retained; physical qualification is open.

    Read the v0.5 reliability comparison · Inspect all three cohorts

  9. Separate faster recovery from an ineffective retry

    A fresh 84-attempt comparison completes 4/12 placements in every mode; all 48 controls fail. Two resumed tasks finish 2.440–2.640 simulated seconds sooner after unchanged settling holds, while four failed grips are refused 2.88–2.90 seconds sooner. Eight failures remain, including two held blocks below the resume-height gate. Every native trajectory and raw computation duration is retained; physical qualification is open.

    Read the v0.4 recovery comparison · Inspect every paired attempt

  10. Compare probe-only motion with contact history

    A frozen 84-attempt paired cohort completes 6/12 history placements versus 4/12 original and probe-only placements; all 48 controls fail. Four tasks complete after five closed holds/resumes, but all six supported acquisition retries fail. Only four of five resume windows meet the independent low absolute tool-speed check. Actual motion and 75 missed computation windows remain visible; physical robot qualification is open.

    Read the contact history comparison · Inspect all 84 attempts

  11. Test the grip and publish a completion regression

    A slow probe and one supported retry retain 30 valid attempts. At moderate friction baseline completes 2/2 while guarded completes 0/2; normal cases both pass and low-friction cases both fail. No successful recovery or overall improvement is demonstrated. Four brief following windows do not establish detector accuracy; 53 computation windows miss 20 ms. Every full attempt remains public.

    Read the paired results and actual stop motion · Inspect all 30 attempts

  12. Measure PiPER contact and a friction failure

    A frozen authored rigid-block pilot retains nine valid attempts: nominal placement 2/2, low-friction placement 0/1, open-jaw 0/3 and hold 0/3. One of 9,000 computation windows exceeds 20 ms. Complete task receipts remain distinct from unloaded profiles; learned skills, shoe physics and physical qualification are open.

    Read the PiPER contact pilot · Open the Robot Simulation Lab

  13. Run more arms on a local CPU

    PiPER and SO-101 join the library with pinned models, explicit arm/gripper units, preserved licenses and complete numerical receipts. Each completes six unloaded motion checks offline. Their learned-policy and physical qualification remain open. The PiPER contact pilot and UR5e grasp demonstration have separate task receipts.

    Read the profile measurements · Open the Robot Simulation Lab

  14. Give the reference arm a contact task

    A free rigid block must be grasped, lifted, carried, released and left settled at its destination. Frozen scenarios compare the authored controller with identical arm commands and open jaws, and with a hold control. Full traces and 3D replay preserve the evidence for the next compact action-learning experiment. Shoe physics, a learned task policy and hardware transfer remain open.

    Read the native task benchmark · Replay grasp and placement

  15. Build a hardware reference we can measure

    A pinned six-axis arm and coupled gripper expose fourteen coordinates and seven commands. All 18 unloaded numerical runs are valid; 1 ms CPU physics measures 40.80–47.29× elapsed-time throughput with sensing, rendering and policy excluded. A paired trace scorer makes physical mismatch measurable. Five-finger references and LeRobot collection have separate contracts; hardware fidelity and shoe competence remain unmeasured.

    Read the mechanical contract and upcoming arms · Open the 3D replay

  16. Learn a compact command-to-motion predictor

    A 6,438-byte CPU model predicts the next 20 ms of joint/jaw movement from native state and issued commands. Twelve seeded groups retain 36 recordings; 4,553 test transitions are not independent trials. Warm computation measures 0.207 ms p95, but jaw mean absolute/worst errors regress against persistence and the first opening is overpredicted. Shoe collection, hardware sensing and validated shoe physics remain open.

    Read the measured pilot and shoe collection plan

  17. Observe image motion and forecast its path

    A manually seeded CPU source alpha records pixel speed and causal forecasts. Six authored cases process 720 frames in 1.454 seconds; occlusion and low-texture losses remain visible, and a 500 ms reversal forecast misses by nearly 90 pixels. The v0.1 release/demo remain separate; real-scene accuracy, metric speed and live robot control are unqualified.

    Read the source alpha and measured limitations

  18. Publish the robotics research pipeline

    A diagram connects footage review, measured robot state, local training, frozen simulation evaluation and compatible skill installation. Automatic annotation and a learned task supervisor are proposed; their companion validation studies and physical robot qualification remain unrun.

    Read the architecture and validation gates

  19. A public skill link reaches a simulated arm

    One-click local installation binds a DVIDIA placement envelope to an authored contact controller. Six scene layouts pass; identical joint commands with open jaws and no-motion controls each pass 0/6. Physical transfer and learned policies remain open.

    Read the installation contract and arm replay

  20. The offline cable environment is runnable

    A local controller-training loop and simulation-only skill pack now run on CPU. Trained and baseline feedback each pass 10/10 simulated seeds; zero force and early release each pass 0/10. Real gripping, calibration and GPU qualification remain open.

    Read the first product build and replay

  21. Contact accuracy meets runtime

    54 deterministic CPU fixture trials recorded. Endpoint agreement coexists with unqualified force transients; coarse wall tests expose missed contact. No GPU or physical-transfer claim.

    Read the measurements

  22. Compare the observation-to-skill thesis with prior work

    Spatial attention, demonstration coverage, executable simulation and reusable repairs are reviewed separately. Three parallel Grok 4.7 searches support discovery; primary sources support the claims.

    Read the comparison

  23. Five licensed POV excerpts processed

    Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.

    Read the feasibility run

  24. Define the evidence and release questions

    Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.

    Read the agenda

Open questions

Follow new publications through RSS or inspect the source and revisions on GitHub.

`; await writePage("index.html", layout({ page: "index.html", title: "Research journal", description: "An open notebook on demonstrations, physical grounding, calibrated simulation and qualified robot skills.", content, catalog, topics, active: "index" })); pages.unshift({ path: "index.html", date: catalog.publications.reduce((latest, paper) => paper.date > latest ? paper.date : latest, "2026-10-07") }); const rss = `\nDVIDIA Research${ORIGIN}/Research footprints on demonstrations, calibrated simulation and robot skills.en${catalog.publications.map(p => { const url = `${ORIGIN}/papers/${p.slug}/`; return `${escapeHtml(p.title)}${url}${url}${new Date(`${p.date}T12:00:00Z`).toUTCString()}${escapeHtml(`${p.status}. ${p.abstract}`)}`; }).join("")}\n`; diff --git a/scripts/image-goal-publication.test.mjs b/scripts/image-goal-publication.test.mjs new file mode 100644 index 0000000..2857149 --- /dev/null +++ b/scripts/image-goal-publication.test.mjs @@ -0,0 +1,103 @@ +import assert from 'node:assert/strict'; +import { createHash } from 'node:crypto'; +import { readFile, readdir } from 'node:fs/promises'; +import { resolve } from 'node:path'; +import test from 'node:test'; + +const ROOT = resolve(import.meta.dirname, '..'); +const LAB = resolve(ROOT, 'topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab'); +const SOURCE = '19acf0734d63ecebc07140c8d84302a54baee4c4'; +const hash = raw => createHash('sha256').update(raw).digest('hex'); +const json = async name => JSON.parse(await readFile(resolve(LAB, name))); + +async function files(directory) { + const names = []; + for (const entry of await readdir(directory, { withFileTypes: true })) { + assert.ok(!entry.isSymbolicLink()); + const path = resolve(directory, entry.name); + if (entry.isDirectory()) names.push(...await files(path)); + else { assert.ok(entry.isFile()); names.push(path.slice(LAB.length + 1)); } + } + return names; +} + +test('image-goal publication preserves distinct native, evaluation and complete package seals', async () => { + const manifest = await json('publication-integrity.json'); + assert.deepEqual((await files(LAB)).filter(name => name !== 'publication-integrity.json').sort(), Object.keys(manifest.files).sort()); + for (const [name, expected] of Object.entries(manifest.files)) { + const raw = await readFile(resolve(LAB, name)); + assert.equal(raw.length, expected.bytes, name); assert.equal(hash(raw), expected.sha256, name); + assert.ok(raw.length < 4 * 1024 * 1024, name); + } + const native = await json('integrity.json'); + for (const file of native.files) { + const raw = await readFile(resolve(LAB, file.path)); + assert.equal(raw.length, file.bytes, file.path); assert.equal(hash(raw), file.sha256, file.path); + } + const audit = await json('native-validation.json'); + const provenance = await json('provenance.json'); + assert.equal(audit.status, 'passed'); + assert.equal(audit.case_count, 32); + assert.equal(audit.physical_complete, 8); + assert.equal(audit.physical_incomplete, 24); + assert.equal(audit.source_digests.verifier_sha256, hash(await readFile(resolve(LAB, 'verify-image-goal-fixtures.py')))); + assert.equal(provenance.publication_source_commit, '3d3c8f660f6f9544d6a58c654bab143dd1eddc00'); + const plan = await json('evaluation-plan.json'); + assert.equal(plan.source_commit, SOURCE); + assert.equal(plan.model, 'jev-1.14.0'); + assert.equal(plan.trials.length, 64); + for (const [name, expected] of Object.entries(plan.files)) { + const raw = await readFile(resolve(LAB, name)); + assert.equal(raw.length, expected.bytes, name); assert.equal(hash(raw), expected.sha256, name); + } + assert.equal((await readFile(resolve(LAB, 'protocol.sha256'), 'utf8')).trim(), hash(await readFile(resolve(LAB, 'protocol.json')))); +}); + +test('all authored conditions and identical-image physical counterexamples remain public', async () => { + const cases = await json('cases.json'); + assert.equal(cases.cases.length, 32); + assert.equal(new Set(cases.cases.map(row => row.layout_id)).size, 4); + const categories = new Map(); + for (const row of cases.cases) categories.set(row.category, (categories.get(row.category) ?? 0) + 1); + assert.equal(categories.size, 8); assert.ok([...categories.values()].every(count => count === 4)); + assert.equal(cases.cases.filter(row => row.physical_truth.complete).length, 8); + assert.equal(cases.cases.filter(row => !row.physical_truth.present).length, 8); + assert.equal(cases.cases.filter(row => row.expected_verdict.rgb === 'unknown').length, 16); + assert.equal(cases.aliases.length, 4); + const byId = new Map(cases.cases.map(row => [row.id, row])); + for (const alias of cases.aliases) { + const moving = byId.get(alias.case_id), settled = byId.get(alias.pixel_identical_to); + assert.equal(moving.physical_truth.complete, false); assert.equal(settled.physical_truth.complete, true); + assert.deepEqual(await readFile(resolve(LAB, moving.current_image)), await readFile(resolve(LAB, settled.current_image))); + assert.equal(moving.expected_verdict.rgb, 'unknown'); assert.equal(settled.expected_verdict.rgb, 'unknown'); + const plan = await json('evaluation-plan.json'); + const trial = (id, condition) => plan.trials.find(row => row.case_id === id && row.condition === condition); + assert.equal(trial(moving.id, 'rgb').request_sha256, trial(settled.id, 'rgb').request_sha256); + assert.notEqual(trial(moving.id, 'rgb_telemetry').request_sha256, trial(settled.id, 'rgb_telemetry').request_sha256); + } +}); + +test('provider status, planned denominators and research claims agree', async () => { + const results = await json('results.json'), publicReport = await json('report.json'), plan = await json('evaluation-plan.json'); + assert.equal(results.evaluation_plan_sha256, hash(await readFile(resolve(LAB, 'evaluation-plan.json')))); + assert.equal(publicReport.status, results.status); + const identity = row => `${row.case_id}/${row.condition}`; + assert.deepEqual(results.results.map(identity).sort(), plan.trials.map(identity).sort()); + assert.deepEqual(publicReport.results.map(identity).sort(), plan.trials.map(identity).sort()); + const paper = await readFile(resolve(ROOT, 'reports/Image goal Jev v01.md'), 'utf8'); + const catalog = JSON.parse(await readFile(resolve(ROOT, 'publications.json'))); + assert.equal(catalog.contributor, null); + assert.equal(catalog.follow, 'https://x.com/iammrriver'); + assert.ok(catalog.publications.some(row => row.slug === 'image-goal-jev-v01')); + assert.ok(paper.includes(SOURCE)); + if (results.status === 'not_run') { + assert.equal(results.metrics, null); + assert.ok(results.results.every(row => row.status === 'not_run' && row.attempts === 0)); + assert.ok(publicReport.results.every(row => row.status === 'not_run' && row.verdict === null && row.p_complete === null)); + assert.ok(publicReport.summary.every(row => row.attempted === 0 && row.successful_response_latency_ms.p95 === null)); + assert.ok(paper.includes('**Jev has not been run.**')); + } else { + assert.equal(results.status, 'completed'); + assert.ok(results.metrics); + } +}); diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE new file mode 100644 index 0000000..a7056f2 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 DVIDIA contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md new file mode 100644 index 0000000..8d6b263 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md @@ -0,0 +1,56 @@ +# Image-goal verification: native benchmark and Jev protocol + +DVIDIA robotics research · 18 October 2026. + +**Jev has not been run.** No TypeSafe credential was available for this execution. All 64 planned provider trials remain `not_run`; model accuracy, false-completion rates, service latency and costs are unmeasured. The report does not turn unavailable inference into zero-error results. + +The completed local work generates 32 native PiPER scene fixtures: eight conditions across four deterministic layouts. Four moving/settled pairs have identical images and different physical completion labels. This is a controlled observation diagnostic, not a learned policy or hardware test. The rigid shoe proxy is deliberately narrow (half width) and 30 g; it is not a full-size calibrated shoe. + +Open `index.html` to compare current/goal images and all case labels offline. `cases.json` preserves privileged physical truth and the declared expected judgment for each input condition. `states/` preserves every authored state, measured native history and physical reference. `evaluation-plan.json` freezes 64 semantic requests, prompt/source/image hashes, scoring and no-retry rules before provider use. `results.json` preserves the explicit unrun rows while omitting raw provider response fields. + +`integrity.json` is the original unmodified native fixture inventory. `report-integrity.json` binds the offline viewer. `publication-integrity.json` binds the complete public package. These are distinct seals; none replaces an earlier inventory. + +## Reproduce + +Clone Dvidia-Inference/dvidia-training and check out publication commit `3d3c8f660f6f9544d6a58c654bab143dd1eddc00`. The native generator and evaluator remain frozen at core commit `19acf0734d63ecebc07140c8d84302a54baee4c4`; the later commit adds the viewer, publisher and independent verifier. Install Python 3.12+ and the repository's `.[arm]` extra. The PiPER model assets are fetched once by the existing robot profile setup and verified against the asset lock. Native simulation and viewer rendering then run locally. See `docs/image-goal-benchmark.md` in the source for complete commands, secure credential setup, model version and limits. + +The passive evaluator can be run with `python -m dvidia_training.image_goal_benchmark evaluate --fixtures PATH --output NEW_DIRECTORY`. It reads `TYPESAFE_API_KEY` or an explicitly named Keychain entry; never paste a token into a public report or source file. Hosted Jev requires network access and a TypeSafe account. The supplied benchmark data and static viewer run without a credential. This publication contains no model weights. + +## Independently check the native evidence + +`native-validation.json` records the completed independent audit of all 32 physical labels and full-window telemetry extrema. `verify-image-goal-fixtures.py` recomputes those values from the saved native records; it does not call Jev, render images, or run a new physical simulation. Its source hash is recorded in both the audit and publication provenance. + +After installing the pinned source above, extract only the original sealed fixture files to a fresh directory. From this downloaded package directory: + +```sh +python - <<'PYTHON' +import hashlib, json +from pathlib import Path, PurePosixPath +source = Path.cwd() +destination = source.parent / "image-goal-native-fixtures" +destination.mkdir(exist_ok=False) +inventory = json.loads((source / "integrity.json").read_text()) +for item in inventory["files"]: + relative = PurePosixPath(item["path"]) + assert not relative.is_absolute() and ".." not in relative.parts + data = (source / relative).read_bytes() + assert len(data) == item["bytes"] + assert hashlib.sha256(data).hexdigest() == item["sha256"] + target = destination / relative + target.parent.mkdir(parents=True, exist_ok=True) + target.write_bytes(data) +for name in ("integrity.json", "evaluation-plan.json"): + (destination / name).write_bytes((source / name).read_bytes()) +PYTHON +python verify-image-goal-fixtures.py --fixtures ../image-goal-native-fixtures --output ../native-validation-reproduced.json +``` + +The verifier intentionally rejects extra files inside the native fixture directory. Keeping the report and its publication metadata outside that directory preserves the original manifest boundary. Expected result: 32 checked cases, 8 physically complete, 24 incomplete, 67 original files verified. Host metadata in a reproduction can differ. + +## Licenses and scope + +Original DVIDIA code and authored synthetic fixture records: MIT (`LICENSE`). The PiPER simulation asset is from MuJoCo Menagerie commit `0059d4335f8156206f63a35662313385f7ad6d74`; its MIT notice is preserved in `licenses/agilex_piper/LICENSE`. No MuJoCo runtime or upstream mesh files are redistributed in this viewer. DVIDIA marks are brand identifiers; the code license grants no trademark rights. + +Jev is TypeSafe's hosted model and retains its own service terms. Its output is evaluated, not used to train or distill a DVIDIA policy. The two model-input conditions are RGB images alone and RGB plus privileged simulator measurements. The latter supplies object identity/presence, pose errors and complete native-window extrema; it is not a demonstrated real-camera or contact estimator. There is no direct motor-control path. + +[Research](https://research.dvidia.org/papers/image-goal-jev-v01/) · [Source](https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4) · [Hugging Face](https://huggingface.co/spaces/Dvidia/robot-simulation-lab) diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json new file mode 100644 index 0000000..9b4807c --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json @@ -0,0 +1,2580 @@ +{ + "schema": "dvidia.image-goal-scenes/v1", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "cases": [ + { + "id": "layout-01-complete", + "category": "complete", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-complete.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741727623932e-07, + "max": 1.0856805225155038e-07 + }, + "yaw_error_rad": { + "min": 2.196340269667258e-09, + "max": 2.1965561156353097e-09 + }, + "linear_speed_m_s": { + "min": 9.860075820885874e-10, + "max": 1.0577925091606234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627744970784057e-09, + "max": 2.046823336044342e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886606, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6192, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.51875, + 0.6578125, + 0.7625 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-missing_target", + "category": "missing_target", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-missing_target.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-01-wrong_object", + "category": "wrong_object", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_object.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-01-wrong_position", + "category": "wrong_position", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_position.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5699999772538076, + -0.10000010615852632, + -0.00010773874568926917 + ], + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.1699999772538407, + "max": 0.1699999772540272 + }, + "yaw_error_rad": { + "min": 2.1963402702132865e-09, + "max": 2.1965561161782934e-09 + }, + "linear_speed_m_s": { + "min": 9.860075879684076e-10, + "max": 1.057792509173234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747009029746e-09, + "max": 2.0468233362534212e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886605, + "max": 0.29429999900143505 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 7452, + "target_box_xyxy_normalized": [ + 0.5453125, + 0.6291666666666667, + 0.8, + 0.9041666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-wrong_orientation.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.40000010615852655, + -0.10000002274619275, + -0.00010773874568926942 + ], + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09, + "table_support_force_n": 0.2942999990014349, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741754859924e-07, + "max": 1.0856805256849158e-07 + }, + "yaw_error_rad": { + "min": 1.57079632459834, + "max": 1.5707963245985561 + }, + "linear_speed_m_s": { + "min": 9.860076309132707e-10, + "max": 1.057792509120737e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627746996178486e-09, + "max": 2.0468233360765137e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.2942999990014349 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 5574, + "target_box_xyxy_normalized": [ + 0.4375, + 0.5, + 0.63125, + 0.7791666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-unreleased", + "category": "unreleased", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-unreleased.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "supported": true, + "released": false, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.40145485082066407, + -0.09444160792298799, + -0.00010962509298383693 + ], + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149, + "table_support_force_n": 0.2995624074490855, + "robot_contact_force_n": 0.7619265340093823, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.00574563428980446, + "max": 0.005753664112566862 + }, + "yaw_error_rad": { + "min": 0.08437097640564, + "max": 0.08440615838439973 + }, + "linear_speed_m_s": { + "min": 9.643102940812748e-05, + "max": 9.969011014506973e-05 + }, + "angular_speed_rad_s": { + "min": 0.003462491733548149, + "max": 0.005988177262940627 + }, + "table_support_force_n": { + "min": 0.2995624074490855, + "max": 0.30103792478353864 + }, + "robot_contact_force_n": { + "min": 0.760598912244461, + "max": 0.7619265340093823 + } + } + }, + "visibility": { + "visible_target_pixels": 5914, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.5229166666666667, + 0.665625, + 0.75 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-moving_alias", + "category": "moving_alias", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-moving_alias.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.3624151236609657, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6192, + "target_box_xyxy_normalized": [ + 0.4296875, + 0.51875, + 0.6578125, + 0.7625 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-01-occluded", + "category": "occluded", + "layout_id": "layout-01", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-01-occluded.png", + "goal_image": "images/layout-01-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741727623932e-07, + "max": 1.0856805225155038e-07 + }, + "yaw_error_rad": { + "min": 2.196340269667258e-09, + "max": 2.1965561156353097e-09 + }, + "linear_speed_m_s": { + "min": 9.860075820885874e-10, + "max": 1.0577925091606234e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627744970784057e-09, + "max": 2.046823336044342e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886606, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-01-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-complete", + "category": "complete", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-complete.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741745389504e-07, + "max": 1.0856805243386722e-07 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007602226177e-10, + "max": 1.0577925091174393e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747344185066e-09, + "max": 2.0468233361619074e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866115, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6084, + "target_box_xyxy_normalized": [ + 0.3921875, + 0.5708333333333333, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-missing_target", + "category": "missing_target", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-missing_target.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-02-wrong_object", + "category": "wrong_object", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_object.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-02-wrong_position", + "category": "wrong_position", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_position.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5999999858101832, + -0.16000010763675523, + -0.00010773874568926928 + ], + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.1699999858102173, + "max": 0.1699999858103543 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007596264554e-10, + "max": 1.0577925091117024e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627749443387563e-09, + "max": 2.0468233361806336e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886612, + "max": 0.29429999900143505 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6701, + "target_box_xyxy_normalized": [ + 0.4765625, + 0.7229166666666667, + 0.7015625, + 1.0 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-wrong_orientation.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4300001076367553, + -0.16000001418981774, + -0.0001077387456892687 + ], + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674174082394e-07, + "max": 1.0856805246501234e-07 + }, + "yaw_error_rad": { + "min": 1.5707963245983363, + "max": 1.5707963245985526 + }, + "linear_speed_m_s": { + "min": 9.86007540976695e-10, + "max": 1.0577925091088337e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774938435085e-09, + "max": 2.04682333632664e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866126, + "max": 0.2942999990014351 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6498, + "target_box_xyxy_normalized": [ + 0.365625, + 0.5833333333333334, + 0.6, + 0.8458333333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-unreleased", + "category": "unreleased", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-unreleased.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.43040182038492686, + -0.1610479818214479, + -0.00010842273593493796 + ], + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277, + "table_support_force_n": 0.3014209771616913, + "robot_contact_force_n": 0.7693653082066636, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0010952390133150847, + "max": 0.00112237494618688 + }, + "yaw_error_rad": { + "min": 0.005609244269984187, + "max": 0.00569434757841164 + }, + "linear_speed_m_s": { + "min": 0.0001367889163867079, + "max": 0.00014160096890228054 + }, + "angular_speed_rad_s": { + "min": 0.0009941703968526277, + "max": 0.0017186694278268843 + }, + "table_support_force_n": { + "min": 0.3014209771616913, + "max": 0.3046279922938624 + }, + "robot_contact_force_n": { + "min": 0.7684190832360072, + "max": 0.7693653082066636 + } + } + }, + "visibility": { + "visible_target_pixels": 5816, + "target_box_xyxy_normalized": [ + 0.390625, + 0.5729166666666666, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-moving_alias", + "category": "moving_alias", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-moving_alias.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.36239906646539655, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6084, + "target_box_xyxy_normalized": [ + 0.3921875, + 0.5708333333333333, + 0.5953125, + 0.8666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-02-occluded", + "category": "occluded", + "layout_id": "layout-02", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-02-occluded.png", + "goal_image": "images/layout-02-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741745389504e-07, + "max": 1.0856805243386722e-07 + }, + "yaw_error_rad": { + "min": 2.1963403873392906e-09, + "max": 2.1965563118397924e-09 + }, + "linear_speed_m_s": { + "min": 9.86007602226177e-10, + "max": 1.0577925091174393e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747344185066e-09, + "max": 2.0468233361619074e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866115, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-02-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-complete", + "category": "complete", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-complete.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741726656525e-07, + "max": 1.0856805227409043e-07 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.860076492373484e-10, + "max": 1.0577925090833765e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774894432646e-09, + "max": 2.046823336032993e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886608, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6191, + "target_box_xyxy_normalized": [ + 0.4359375, + 0.4875, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-missing_target", + "category": "missing_target", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-missing_target.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-03-wrong_object", + "category": "wrong_object", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_object.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-03-wrong_position", + "category": "wrong_position", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_position.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5499999688429305, + -0.07000010400124504, + -0.00010773874568926812 + ], + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.16999996884296226, + "max": 0.1699999688431964 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.86007647555542e-10, + "max": 1.0577925090868942e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774718512132e-09, + "max": 2.0468233361579335e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 7062, + "target_box_xyxy_normalized": [ + 0.5703125, + 0.5583333333333333, + 0.828125, + 0.7666666666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-wrong_orientation.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3800001040012452, + -0.07000003115707004, + -0.00010773874568926854 + ], + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09, + "table_support_force_n": 0.29429999900143444, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741730644736e-07, + "max": 1.0856805243889215e-07 + }, + "yaw_error_rad": { + "min": 1.570796324598342, + "max": 1.570796324598559 + }, + "linear_speed_m_s": { + "min": 9.86007598072673e-10, + "max": 1.0577925090957944e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747152164955e-09, + "max": 2.0468233360100356e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866126, + "max": 0.29429999900143444 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 4876, + "target_box_xyxy_normalized": [ + 0.4703125, + 0.44375, + 0.625, + 0.7333333333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-unreleased", + "category": "unreleased", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-unreleased.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.38020236953882225, + -0.0693768704293019, + -9.617134314363833e-05 + ], + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283, + "table_support_force_n": 0.2639875277923961, + "robot_contact_force_n": 0.7863052928276089, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0006474789378581962, + "max": 0.0006551670719148867 + }, + "yaw_error_rad": { + "min": 0.003703847249796388, + "max": 0.003720938606997237 + }, + "linear_speed_m_s": { + "min": 0.00010986191904928065, + "max": 0.00014294490050929373 + }, + "angular_speed_rad_s": { + "min": 0.0024920357101732454, + "max": 0.0040346175968384084 + }, + "table_support_force_n": { + "min": 0.25922471284709236, + "max": 0.2640607875145524 + }, + "robot_contact_force_n": { + "min": 0.7857892493442301, + "max": 0.7878949274134518 + } + } + }, + "visibility": { + "visible_target_pixels": 5657, + "target_box_xyxy_normalized": [ + 0.4375, + 0.48541666666666666, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-moving_alias", + "category": "moving_alias", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-moving_alias.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.362350885628384, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 6191, + "target_box_xyxy_normalized": [ + 0.4359375, + 0.4875, + 0.675, + 0.6854166666666667 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-03-occluded", + "category": "occluded", + "layout_id": "layout-03", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-03-occluded.png", + "goal_image": "images/layout-03-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741726656525e-07, + "max": 1.0856805227409043e-07 + }, + "yaw_error_rad": { + "min": 2.1963403318281394e-09, + "max": 2.19655620081749e-09 + }, + "linear_speed_m_s": { + "min": 9.860076492373484e-10, + "max": 1.0577925090833765e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774894432646e-09, + "max": 2.046823336032993e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886608, + "max": 0.2942999990014348 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-03-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-complete", + "category": "complete", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-complete.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674170840469e-07, + "max": 1.0856805211389336e-07 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.860076016164131e-10, + "max": 1.057792509081208e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627745907984584e-09, + "max": 2.046823336175752e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 5693, + "target_box_xyxy_normalized": [ + 0.3984375, + 0.5395833333333333, + 0.5796875, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-complete.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-missing_target", + "category": "missing_target", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-missing_target.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-missing_target.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-04-wrong_object", + "category": "wrong_object", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_object.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": null, + "yaw_error_rad": null, + "supported": false, + "released": true, + "linear_speed_m_s": null, + "angular_speed_rad_s": null + }, + "measured_inputs": { + "target_present": false, + "target_position_m": null, + "position_error_m": null, + "yaw_error_rad": null, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": 0.0, + "max": 0.0 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_object.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "absent", + "rgb_telemetry": "absent" + } + }, + { + "id": "layout-04-wrong_position", + "category": "wrong_position", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_position.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09, + "table_support_force_n": 0.29429999900143494, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.16999999337338886, + "max": 0.16999999337348212 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.86007605782391e-10, + "max": 1.0577925090883427e-06 + }, + "angular_speed_rad_s": { + "min": 2.762774742684285e-09, + "max": 2.0468233361281146e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.29429999900143494 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6447, + "target_box_xyxy_normalized": [ + 0.4578125, + 0.7020833333333333, + 0.6609375, + 1.0 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_position.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-wrong_orientation", + "category": "wrong_orientation", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-wrong_orientation.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805194707237e-07, + "yaw_error_rad": 1.57079632459834, + "supported": true, + "released": true, + "linear_speed_m_s": 9.86007598437561e-10, + "angular_speed_rad_s": 2.7627747672417554e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4200001083656286, + -0.13000000662664574, + -0.0001077387456892685 + ], + "position_error_m": 1.0856805194707237e-07, + "yaw_error_rad": 1.57079632459834, + "linear_speed_m_s": 9.86007598437561e-10, + "angular_speed_rad_s": 2.7627747672417554e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.0856741703541562e-07, + "max": 1.0856805194707237e-07 + }, + "yaw_error_rad": { + "min": 1.5707963245983396, + "max": 1.5707963245985561 + }, + "linear_speed_m_s": { + "min": 9.86007598437561e-10, + "max": 1.0577925091079773e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627747672417554e-09, + "max": 2.0468233361383416e-06 + }, + "table_support_force_n": { + "min": 0.2942989247886607, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 6485, + "target_box_xyxy_normalized": [ + 0.359375, + 0.5645833333333333, + 0.6015625, + 0.8 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-wrong_orientation.json", + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-unreleased", + "category": "unreleased", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-unreleased.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 0.0010491825564488413, + "yaw_error_rad": 0.03690074331083231, + "supported": true, + "released": false, + "linear_speed_m_s": 0.00010064949189093267, + "angular_speed_rad_s": 0.002606933470747755 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.42104823526500446, + -0.13004457427461588, + -0.00011458521544506569 + ], + "position_error_m": 0.0010491825564488413, + "yaw_error_rad": 0.03690074331083231, + "linear_speed_m_s": 0.00010064949189093267, + "angular_speed_rad_s": 0.002606933470747755, + "table_support_force_n": 0.3144476609893772, + "robot_contact_force_n": 0.790406083741399, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 0.0010350014788722895, + "max": 0.0010491825564488413 + }, + "yaw_error_rad": { + "min": 0.03690074331083231, + "max": 0.03691679299236298 + }, + "linear_speed_m_s": { + "min": 9.490361836146501e-05, + "max": 0.00010064949189093267 + }, + "angular_speed_rad_s": { + "min": 0.002606933470747755, + "max": 0.004742951667749043 + }, + "table_support_force_n": { + "min": 0.3142693700788355, + "max": 0.3148922546022384 + }, + "robot_contact_force_n": { + "min": 0.7881504132862833, + "max": 0.790406083741399 + } + } + }, + "visibility": { + "visible_target_pixels": 5582, + "target_box_xyxy_normalized": [ + 0.396875, + 0.5416666666666666, + 0.584375, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-unreleased.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-moving_alias", + "category": "moving_alias", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-moving_alias.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.3623192350084462, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.0, + "native_timestep_s": 0.002, + "native_observation_count": 0, + "target_present_observation_count": 0, + "native_window_extrema": { + "position_error_m": { + "min": null, + "max": null + }, + "yaw_error_rad": { + "min": null, + "max": null + }, + "linear_speed_m_s": { + "min": null, + "max": null + }, + "angular_speed_rad_s": { + "min": null, + "max": null + }, + "table_support_force_n": { + "min": null, + "max": null + }, + "robot_contact_force_n": { + "min": null, + "max": null + } + } + }, + "visibility": { + "visible_target_pixels": 5693, + "target_box_xyxy_normalized": [ + 0.3984375, + 0.5395833333333333, + 0.5796875, + 0.8395833333333333 + ], + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-moving_alias.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + }, + "expected_presence": { + "rgb": "present", + "rgb_telemetry": "present" + } + }, + { + "id": "layout-04-occluded", + "category": "occluded", + "layout_id": "layout-04", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "current_image": "images/layout-04-occluded.png", + "goal_image": "images/layout-04-complete.png", + "physical_truth": { + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "supported": true, + "released": true, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09 + }, + "measured_inputs": { + "target_present": true, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "position_error_m": 1.0856805211389336e-07, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "angular_speed_rad_s": 2.7627745907984584e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "observation_window_s": 0.2, + "native_timestep_s": 0.002, + "native_observation_count": 100, + "target_present_observation_count": 100, + "native_window_extrema": { + "position_error_m": { + "min": 1.085674170840469e-07, + "max": 1.0856805211389336e-07 + }, + "yaw_error_rad": { + "min": 2.196340442850442e-09, + "max": 2.1965565755177607e-09 + }, + "linear_speed_m_s": { + "min": 9.860076016164131e-10, + "max": 1.057792509081208e-06 + }, + "angular_speed_rad_s": { + "min": 2.7627745907984584e-09, + "max": 2.046823336175752e-06 + }, + "table_support_force_n": { + "min": 0.29429892478866077, + "max": 0.294299999001435 + }, + "robot_contact_force_n": { + "min": 0.0, + "max": 0.0 + } + } + }, + "visibility": { + "visible_target_pixels": 0, + "target_box_xyxy_normalized": null, + "box_scope": "Visible target pixels only, from native geom-ID segmentation; not an amodal box." + }, + "state_file": "states/layout-04-occluded.json", + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + }, + "expected_presence": { + "rgb": "unknown", + "rgb_telemetry": "present" + } + } + ], + "aliases": [ + { + "case_id": "layout-01-moving_alias", + "pixel_identical_to": "layout-01-complete" + }, + { + "case_id": "layout-02-moving_alias", + "pixel_identical_to": "layout-02-complete" + }, + { + "case_id": "layout-03-moving_alias", + "pixel_identical_to": "layout-03-complete" + }, + { + "case_id": "layout-04-moving_alias", + "pixel_identical_to": "layout-04-complete" + } + ], + "assets": { + "profile": "piper", + "repository": "https://github.com/google-deepmind/mujoco_menagerie", + "commit": "0059d4335f8156206f63a35662313385f7ad6d74", + "files_verified": 90, + "bytes_verified": 34562302, + "lock_sha256": "4b2ee2ca675aceb2f826983cb3082a3e08e9aa0ff419b23078abcff08ebcc6fe", + "simulation_asset_license": "MIT" + }, + "protocol_sha256": "d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4", + "mujoco_version": "3.15.0", + "rendering": "Native MuJoCo RGB and geom segmentation; no image-generation model.", + "generation_wall_seconds": 6.2972065838985145, + "scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator." +} diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json new file mode 100644 index 0000000..8e7c622 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json @@ -0,0 +1,643 @@ +{ + "automatic_retries": 0, + "cohort": "Authored diagnostic fixtures; four scene layouts are correlated clusters, not 32 independent real-world tasks.", + "completion_decision": "Unmodified Jev Choice verdict; unknown is abstention.", + "conditions": [ + "rgb", + "rgb_telemetry" + ], + "cost_scope": "Planning allowance assumes documented roughly 64k default context. 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DVIDIA
+

Robotics research · Image goals / 0.1

Does it only look finished?

A goal image describes the destination. This experiment asks Jev to judge the current scene, then compares its answer with the simulator’s physical state. Missing shoes and misleading arrangements stay in the results.

Loading the recorded experiment.

+

Current simulated scene
Current observation
Requested goal arrangement
Goal reference

Physical reference

Recorded simulator labels. A single image may not reveal hidden contact or sustained support.

Jev judgment

A remote research judgment. This page sends no images, runs no model and commands no robot.

+

Count the misses. Keep the clock honest.

Judgment accuracy uses the frozen, observability-aware verdict for every planned case: complete, incomplete, or unknown. A correct abstention matters when an image cannot establish hidden motion, contact or dwell. Errors and cases not run remain in the planned denominator; no score is claimed before requests are attempted. Physical false-completion and missing-target counts retain every planned negative case, with answered coverage shown separately.

Decision checkCorrectDenominator
Latency scopeTimed requestsp50p95p99Maximum

Timing is recorded wall-clock latency, not the robot’s reaction time or simulator timestep. Quantiles use linear interpolation and retain timed errors in the all-attempt row. A small cohort cannot establish production tail latency. Missing measurements remain unreported.

This benchmark uses synthetic simulation fixtures and a hosted decision model. It does not publish trained robot weights or demonstrate hardware transfer. The DVIDIA code license does not grant rights to the DVIDIA marks or to an external model.

Public results · JSONFile checksumsJev image documentation ↗
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b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE new file mode 100644 index 0000000..1ace5a2 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 RosenYin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json new file mode 100644 index 0000000..6a7ea49 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json @@ -0,0 +1,745 @@ +{ + "schema": "dvidia.image-goal-native-validation/v1", + "status": "passed", + "scope": "Independent recomputation from frozen native state measurements/history and raw coordinates. No images re-rendered, no fresh physics run, no model service called and no hardware validation.", + "fixture_generation_scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator.", + "source_digests": { + "protocol_sha256": "d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4", + "cases_sha256": "f8b1b205b16e9833df7bd7063e0572355794dcede49a024f5d73ae6f65bbb1aa", + "integrity_sha256": "e8b6bbe4f2928e85f6e88d16af7d719dc32b1a3b588ce9633805061528cbe682", + "verifier_sha256": "514be68c1dbd9ecb797f46615524f2bffa8654a5df3a62b90f4f8cc897414c08" + }, + "files_verified": 67, + "bytes_verified": 5019443, + "additional_runner_metadata": [ + { + "path": "evaluation-plan.json", + "bytes": 24662, + "sha256": "5f02006190d97bdaca43f139f5bf221cece23bba868a5e9ee0f424618ea87d8f", + "scope": "Runner metadata added after fixture generation; fingerprinted here, not claimed to be covered by the original fixture integrity manifest." + } + ], + "native_generation_wall_seconds": 6.2972065838985145, + "native_generation_timing_scope": "Recorded fixture construction, stepping and rendering; excludes Jev inference and this verification.", + "recorded_mujoco_version": "3.15.0", + "verification_host": { + "system": "Darwin", + "release": "25.5.0", + "machine": "arm64", + "processor": "arm", + "logical_cpu_count": 10, + "python_version": "3.12.9", + "scope": "Host running this verifier; CPU model, RAM and GPU specifications are not inferred." + }, + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "case_count": 32, + "category_counts": { + "complete": 4, + "missing_target": 4, + "moving_alias": 4, + "occluded": 4, + "unreleased": 4, + "wrong_object": 4, + "wrong_orientation": 4, + "wrong_position": 4 + }, + "physical_complete": 8, + "physical_incomplete": 24, + "zero_solver_warnings": true, + "no_added_object_attachment_constraints": true, + "no_recorded_external_forces": true, + "native_equalities_per_model": 1, + "equality_scope": "Recorded upstream gripper coupling count; verified against frozen state metadata, without recompiling models.", + "all_physical_labels_recomputed": true, + "all_telemetry_extrema_recomputed": true, + "unreleased_rgb_unknown_cases": 4, + "aliases": [ + { + "case_id": "layout-01-moving_alias", + "pixel_identical_to": "layout-01-complete", + "png_sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "png_bytes": 79345, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-02-moving_alias", + "pixel_identical_to": "layout-02-complete", + "png_sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "png_bytes": 79868, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-03-moving_alias", + "pixel_identical_to": "layout-03-complete", + "png_sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "png_bytes": 78462, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + }, + { + "case_id": "layout-04-moving_alias", + "pixel_identical_to": "layout-04-complete", + "png_sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "png_bytes": 77762, + "byte_identical": true, + "pose_identical": true, + "velocity_and_completion_differ": true + } + ], + "cases": [ + { + "id": "layout-01-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-01-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.1699999772538407, + "yaw_error_rad": 2.1965561161782934e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075879684076e-10, + "angular_speed_rad_s": 2.7627747009029746e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805256849158e-07, + "yaw_error_rad": 1.5707963245983405, + "table_support_force_n": 0.2942999990014349, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076309132707e-10, + "angular_speed_rad_s": 2.7627746996178486e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.00574563428980446, + "yaw_error_rad": 0.08440615838439973, + "table_support_force_n": 0.2995624074490855, + "robot_contact_force_n": 0.7619265340093823, + "linear_speed_m_s": 9.969011014506973e-05, + "angular_speed_rad_s": 0.003462491733548149, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.3624151236609657, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-01-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805225155038e-07, + "yaw_error_rad": 2.1965561156353097e-09, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860075820885874e-10, + "angular_speed_rad_s": 2.7627744970784057e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-02-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-02-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.1699999858102173, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.29429999900143505, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007596264554e-10, + "angular_speed_rad_s": 2.7627749443387563e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805246501234e-07, + "yaw_error_rad": 1.5707963245983367, + "table_support_force_n": 0.2942999990014351, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007540976695e-10, + "angular_speed_rad_s": 2.762774938435085e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.00112237494618688, + "yaw_error_rad": 0.00569434757841164, + "table_support_force_n": 0.3014209771616913, + "robot_contact_force_n": 0.7693653082066636, + "linear_speed_m_s": 0.00014160096890228054, + "angular_speed_rad_s": 0.0009941703968526277, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.36239906646539655, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-02-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805243386722e-07, + "yaw_error_rad": 2.1965563118397924e-09, + "table_support_force_n": 0.294299999001435, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007602226177e-10, + "angular_speed_rad_s": 2.7627747344185066e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-03-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-03-missing_target", + "category": "missing_target", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_object", + "category": "wrong_object", + "present": false, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": false, + "released": true, + "position_error_m": null, + "yaw_error_rad": null, + "table_support_force_n": 0.0, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": null, + "angular_speed_rad_s": null, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_position", + "category": "wrong_position", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 0.16999996884296226, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007647555542e-10, + "angular_speed_rad_s": 2.762774718512132e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-wrong_orientation", + "category": "wrong_orientation", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805243889215e-07, + "yaw_error_rad": 1.5707963245983425, + "table_support_force_n": 0.29429999900143444, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.86007598072673e-10, + "angular_speed_rad_s": 2.7627747152164955e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "incomplete", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-unreleased", + "category": "unreleased", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": false, + "position_error_m": 0.0006551670719148867, + "yaw_error_rad": 0.003703847249796388, + "table_support_force_n": 0.2639875277923961, + "robot_contact_force_n": 0.7863052928276089, + "linear_speed_m_s": 0.00010986191904928065, + "angular_speed_rad_s": 0.003368756823804283, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-moving_alias", + "category": "moving_alias", + "present": true, + "complete": false, + "physical_conditions_dwell_s": 0.0, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.362350885628384, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 0.12, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 0, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "incomplete" + } + }, + { + "id": "layout-03-occluded", + "category": "occluded", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805227409043e-07, + "yaw_error_rad": 2.19655620081749e-09, + "table_support_force_n": 0.2942999990014348, + "robot_contact_force_n": 0.0, + "linear_speed_m_s": 9.860076492373484e-10, + "angular_speed_rad_s": 2.762774894432646e-09, + "native_history_samples": 100, + "expected_verdict": { + "rgb": "unknown", + "rgb_telemetry": "complete" + } + }, + { + "id": "layout-04-complete", + "category": "complete", + "present": true, + "complete": true, + "physical_conditions_dwell_s": 0.2, + "supported": true, + "released": true, + "position_error_m": 1.0856805211389336e-07, + 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Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target.", + "criteria": { + "position_tolerance_m": 0.025, + "yaw_tolerance_rad": 0.2617993877991494, + "linear_speed_max_m_s": 0.01, + "angular_speed_max_rad_s": 0.05, + "support_force_min_n": 0.01, + "robot_force_max_n": 0.01, + "continuous_dwell_s": 0.2 + }, + "layouts": [ + { + "id": "layout-01", + "xy_m": [ + 0.4, + -0.1 + ], + "yaw_rad": 0.0, + "azimuth_deg": 130.0 + }, + { + "id": "layout-02", + "xy_m": [ + 0.43, + -0.16 + ], + "yaw_rad": 0.08, + "azimuth_deg": 145.0 + }, + { + "id": "layout-03", + "xy_m": [ + 0.38, + -0.07 + ], + "yaw_rad": -0.08, + "azimuth_deg": 115.0 + }, + { + "id": "layout-04", + "xy_m": [ + 0.42, + -0.13 + ], + "yaw_rad": 0.15, + "azimuth_deg": 155.0 + } + ], + "categories": [ + "complete", + "missing_target", + "wrong_object", + "wrong_position", + "wrong_orientation", + "unreleased", + "moving_alias", + "occluded" + ], + "case_ids": [ + "layout-01-complete", + "layout-01-missing_target", + "layout-01-wrong_object", + "layout-01-wrong_position", + "layout-01-wrong_orientation", + "layout-01-unreleased", + "layout-01-moving_alias", + "layout-01-occluded", + "layout-02-complete", + "layout-02-missing_target", + "layout-02-wrong_object", + "layout-02-wrong_position", + "layout-02-wrong_orientation", + "layout-02-unreleased", + "layout-02-moving_alias", + "layout-02-occluded", + "layout-03-complete", + "layout-03-missing_target", + "layout-03-wrong_object", + "layout-03-wrong_position", + "layout-03-wrong_orientation", + "layout-03-unreleased", + "layout-03-moving_alias", + "layout-03-occluded", + "layout-04-complete", + "layout-04-missing_target", + "layout-04-wrong_object", + "layout-04-wrong_position", + "layout-04-wrong_orientation", + "layout-04-unreleased", + "layout-04-moving_alias", + "layout-04-occluded" + ], + "image_size": [ + 640, + 480 + ], + "physics_timestep_s": 0.002, + "settling_s": 0.6, + "native_observation_window_s": 0.2, + "unreleased_controller": { + "stages": [ + "approach above heel", + "descend", + "close" + ], + "stage_duration_s": [ + 3.0, + 2.0, + 1.2 + ], + "heel_offset_m": -0.085, + "tcp_height_m": [ + 0.2, + 0.055, + 0.055 + ], + "jaw_command_m": [ + 0.035, + 0.035, + 0.0 + ], + "native_dt_s": 0.002, + "control_dt_s": 0.02, + "scope": "Authored approach then native finger contact; not a lift or learned manipulation." + }, + "moving_alias_velocity_m_s": [ + 0.12, + 0.0, + 0.0 + ], + "synthetic_shoe": { + "source": "packaged trainer-left collision primitives", + "width_scale": 0.5, + "mass_kg": 0.03, + "visual_colors": "Green upper for target, orange upper for decoy, cream sole and toe.", + "reason": "Declared narrow rigid proxy permits unchanged PiPER finger contact; not a full-size measured shoe." + }, + "scope": "Authored native physical-state verification. Four deterministic layouts, not statistical independent real-world scenes or a trained policy benchmark.", + "dwell_scope": "Every 2 ms native sample satisfies all thresholds for 100 consecutive ticks; no continuous-time or hardware certification claim.", + "reset_scope": "Each fixture starts from an authored state. The moving alias changes native velocity at the snapshot without advancing time; this is a declared state intervention, not a measured physical impulse.", + "telemetry_scope": "Privileged native state and contact history; not an implemented camera or hardware estimator.", + "rgb_observability": "Single RGB cannot prove velocity, support force, release force or 200 ms dwell. Complete, moving, occluded and unreleased states require unknown. Even visibly closed fingers do not establish the 0.01 N contact-force threshold. Missing targets, wrong objects and visible pose violations permit incomplete.", + "freeze": "Generate and hash this protocol before any model requests; never choose cases or thresholds from model results." +} diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 new file mode 100644 index 0000000..a4177f7 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.sha256 @@ -0,0 +1 @@ +d48e363d24cc3c9f3be67ffb87390aa3f9b37272bd28d4b9b22424ccf336fba4 diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json new file mode 100644 index 0000000..cf578ea --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json @@ -0,0 +1,28 @@ +{ + "base_training_commit": 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"not_run": 32, + "completion_accuracy": { + "correct": 0, + "answered": 0, + "planned": 32 + }, + "completion_abstentions": 0, + "observability_aware_accuracy": { + "correct": 0, + "planned": 32 + }, + "known_verdict_accuracy": { + "correct": 0, + "planned": 32 + }, + "ambiguous_abstention": { + "correct": 0, + "planned": 0 + }, + "false_completion": { + "count": 0, + "answered_incomplete": 0, + "planned_incomplete": 24, + "valid_incomplete": 0 + }, + "false_presence": { + "count": 0, + "answered_absent": 0, + "planned_absent": 8, + "valid_absent": 0 + }, + "known_usage_estimated_input_cost_usd": null, + "unknown_cost_attempts": 0, + "all_attempt_latency_ms": { + "n": 0, + "p50": null, + "p95": null, + "p99": null, + "max": null + }, + "successful_response_latency_ms": { + "n": 0, + "p50": null, + "p95": null, + "p99": null, + "max": null + } + } + ], + "scope": "Synthetic simulation fixtures; passive image-goal judgments; no robot control or trained weights." +} diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json new file mode 100644 index 0000000..f638db4 --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json @@ -0,0 +1,8000 @@ +{ + "active_trial": null, + "environment": { + "platform": "macOS-26.5.2-arm64-arm-64bit", + "python": "3.12.9" + }, + "evaluation_plan_sha256": "5f02006190d97bdaca43f139f5bf221cece23bba868a5e9ee0f424618ea87d8f", + "limitations": [ + "Authored diagnostic fixtures; four scene layouts are correlated clusters, not 32 independent real-world tasks.", + "Preloaded image bytes + request construction + image upload + hosted response + parsing; excludes fixture file loading, prerecorded rendering, live camera, actuation and physical task duration.", + "Telemetry is privileged simulator state, not validated real sensor readings.", + "Jev is a hosted experimental image evaluator; no robot learning or local Jev inference is demonstrated." + ], + "metrics": null, + "model": "jev-1.14.0", + "planned_conditions": [ + "rgb", + "rgb_telemetry" + ], + "publication_sanitization": { + "credentials_exported": false, + "omitted_fields": [ + "results[].response" + ], + "raw_provider_response_exported": false + }, + "results": [ + { + "attempts": 0, + "case_id": "layout-02-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81061, + "format": "png", + "height": 480, + "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.24558301083743572, + "total": 0.2466249279677868 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627749443387563e-09, + "linear_speed_m_s": 9.86007596264554e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361806336e-06, + "min": 2.7627749443387563e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091117024e-06, + "min": 9.86007596264554e-10 + }, + "position_error_m": { + "max": 0.1699999858103543, + "min": 0.1699999858102173 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143505, + "min": 0.2942989247886612 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.1699999858102173, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143505, + "target_position_m": [ + 0.5999999858101832, + -0.16000010763675523, + -0.00010773874568926928 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 165351, + "request_sha256": "4b2d21390d91a0e27ed7498506c325fb33f997108f0cd688646df0c8767b06b5", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.038737+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.24558301083743572, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78325, + "format": "png", + "height": 480, + "sha256": "66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16983295790851116, + "total": 0.17058290541172028 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161676, + "request_sha256": "cf2c595e98a0953986643451655b82b56ec9b6797f0385f172992a19a09e2902", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.040171+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16983295790851116, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1689591445028782, + "total": 0.16945903189480305 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162173, + "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.041499+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1689591445028782, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 69903, + "format": "png", + "height": 480, + "sha256": "0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1554158516228199, + "total": 0.15583285130560398 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151848, + "request_sha256": "6dc62c2ce15e117efcf3c31ebf6559225eec602a00771ca335893539e0ce7b17", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.042872+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1554158516228199, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16854191198945045, + "total": 0.16895798034965992 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.362350885628384, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161164, + "request_sha256": "562192137127c05e4d4f824bf7f12fd6f1885900aad2a486b8b2026602dc2e0b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.044259+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16854191198945045, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1835001166909933, + "total": 0.18404098227620125 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336032993e-06, + "min": 2.762774894432646e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090833765e-06, + "min": 9.860076492373484e-10 + }, + "position_error_m": { + "max": 1.0856805227409043e-07, + "min": 1.0856741726656525e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.2942989247886608 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161351, + "request_sha256": "9cdeacbb6a7bfb89a52b1440577dc93a86581c29ac4a50445c64fbb9436a38cc", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.045844+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1835001166909933, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72934, + "format": "png", + "height": 480, + "sha256": "d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15729200094938278, + "total": 0.15762494876980782 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155509, + "request_sha256": "a45d75d5ee6d6d2f3f98afba864906ff46b15fdececc6dfee1f03dffe06c816c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.047550+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15729200094938278, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 85815, + "format": "png", + "height": 480, + "sha256": "9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18979120068252087, + "total": 0.1901660580188036 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 0.0009941703968526277, + "linear_speed_m_s": 0.00014160096890228054, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.0017186694278268843, + "min": 0.0009941703968526277 + }, + "linear_speed_m_s": { + "max": 0.00014160096890228054, + "min": 0.0001367889163867079 + }, + "position_error_m": { + "max": 0.00112237494618688, + "min": 0.0010952390133150847 + }, + "robot_contact_force_n": { + "max": 0.7693653082066636, + "min": 0.7684190832360072 + }, + "table_support_force_n": { + "max": 0.3046279922938624, + "min": 0.3014209771616913 + }, + "yaw_error_rad": { + "max": 0.00569434757841164, + "min": 0.005609244269984187 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.00112237494618688, + "robot_contact_force_n": 0.7693653082066636, + "table_support_force_n": 0.3014209771616913, + "target_position_m": [ + 0.43040182038492686, + -0.1610479818214479, + -0.00010842273593493796 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.00569434757841164 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 170145, + "request_sha256": "ba1ab4131973a5d3833b50c1af81ec168dded4ea50209a2b9e88b6d64c07849a", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.049350+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18979120068252087, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14620809815824032, + "total": 0.14654104597866535 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159007, + "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.051252+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14620809815824032, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79569, + "format": "png", + "height": 480, + "sha256": "66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18862495198845863, + "total": 0.18920795992016792 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.7627747152164955e-09, + "linear_speed_m_s": 9.86007598072673e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233360100356e-06, + "min": 2.7627747152164955e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090957944e-06, + "min": 9.86007598072673e-10 + }, + "position_error_m": { + "max": 1.0856805243889215e-07, + "min": 1.0856741730644736e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143444, + "min": 0.29429892478866126 + }, + "yaw_error_rad": { + "max": 1.570796324598559, + "min": 1.570796324598342 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243889215e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143444, + "target_position_m": [ + 0.3800001040012452, + -0.07000003115707004, + -0.00010773874568926854 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983425 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162452, + "request_sha256": "2ea701cfba448a105267bbb27f8303456ca072d2d07a4a930d21034853fb262d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.053912+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18862495198845863, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 86362, + "format": "png", + "height": 480, + "sha256": "8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1669169869273901, + "total": 0.16720802523195744 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 0.002606933470747755, + "linear_speed_m_s": 0.00010064949189093267, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.004742951667749043, + "min": 0.002606933470747755 + }, + "linear_speed_m_s": { + "max": 0.00010064949189093267, + "min": 9.490361836146501e-05 + }, + "position_error_m": { + "max": 0.0010491825564488413, + "min": 0.0010350014788722895 + }, + "robot_contact_force_n": { + "max": 0.790406083741399, + "min": 0.7881504132862833 + }, + "table_support_force_n": { + "max": 0.3148922546022384, + "min": 0.3142693700788355 + }, + "yaw_error_rad": { + "max": 0.03691679299236298, + "min": 0.03690074331083231 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.0010491825564488413, + "robot_contact_force_n": 0.790406083741399, + "table_support_force_n": 0.3144476609893772, + "target_position_m": [ + 0.42104823526500446, + -0.13004457427461588, + -0.00011458521544506569 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.03690074331083231 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 168585, + "request_sha256": "6517dc9913124d034b3a54349b77f81feb05e049b0660a75c9b93d1443d0c425", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.056020+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1669169869273901, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 69903, + "format": "png", + "height": 480, + "sha256": "0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16333302482962608, + "total": 0.16379100270569324 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-0fd28594d7bee6d76cb13dd30e3bfe520918d470fa2bec53ecf84a493aed0556", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774894432646e-09, + "linear_speed_m_s": 9.860076492373484e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336032993e-06, + "min": 2.762774894432646e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090833765e-06, + "min": 9.860076492373484e-10 + }, + "position_error_m": { + "max": 1.0856805227409043e-07, + "min": 1.0856741726656525e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.2942989247886608 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805227409043e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.3799999688429301, + -0.07000010400124504, + -0.00010773874568926843 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 152792, + "request_sha256": "0b9a08881ff8452447d301dc7e4379139afd0cda5b98a0ad7699e03a6e433c53", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.058197+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16333302482962608, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 64921, + "format": "png", + "height": 480, + "sha256": "3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14112493954598904, + "total": 0.1415829174220562 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 148272, + "request_sha256": "1d6c918bd75bbb3d3544876daee9b4f4439c27f76d4a97126c1734d831add576", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.060536+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14112493954598904, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 87865, + "format": "png", + "height": 480, + "sha256": "2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.154959037899971, + "total": 0.1553341280668974 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 169810, + "request_sha256": "803f461156bfe7fc05d5fbbdab899f45c52b561e1700f4c8ba68873e3635d6dd", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.062950+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.154959037899971, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 71367, + "format": "png", + "height": 480, + "sha256": "dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15566707588732243, + "total": 0.15600002370774746 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155348, + "request_sha256": "6e3c7bda63e2584be56d9f5ffbcdf6228ba3f44c1d45e4d53f708f7e873d12da", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.065408+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15566707588732243, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79111, + "format": "png", + "height": 480, + "sha256": "56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16829092055559158, + "total": 0.1686250325292349 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627746996178486e-09, + "linear_speed_m_s": 9.860076309132707e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233360765137e-06, + "min": 2.7627746996178486e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509120737e-06, + "min": 9.860076309132707e-10 + }, + "position_error_m": { + "max": 1.0856805256849158e-07, + "min": 1.0856741754859924e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014349, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 1.5707963245985561, + "min": 1.57079632459834 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805256849158e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014349, + "target_position_m": [ + 0.40000010615852655, + -0.10000002274619275, + -0.00010773874568926942 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983405 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162876, + "request_sha256": "5427dbcbe70b437b4949c2ef974905d4710ea0c994e8d92fd207d1017d494d6d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.068035+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16829092055559158, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 86362, + "format": "png", + "height": 480, + "sha256": "8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16229203902184963, + "total": 0.16262498684227467 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-8d0290813f4288bddf5287eaf2d7e5b12e3710a284a5028fe229d57da3c4c736", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167607, + "request_sha256": "053c7b9d62bc72c6fccfee44a071cc0cb5c9fc4a09cef15f568ae958532d7e8e", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.070736+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16229203902184963, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 84090, + "format": "png", + "height": 480, + "sha256": "3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15604193322360516, + "total": 0.15637511387467384 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 166918, + "request_sha256": "29650676bc836d7065cc13f79ef3e1dbdadc7a139bfa45564dfd337c24f4918c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.073495+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15604193322360516, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 62348, + "format": "png", + "height": 480, + "sha256": "29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15787500888109207, + "total": 0.1581660471856594 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336175752e-06, + "min": 2.7627745907984584e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509081208e-06, + "min": 9.860076016164131e-10 + }, + "position_error_m": { + "max": 1.0856805211389336e-07, + "min": 1.085674170840469e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 144539, + "request_sha256": "ec0eb371382c7fd7b35408483f2c9978fce56c17d2f6355bb4027ed68d4977af", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.076313+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15787500888109207, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 62348, + "format": "png", + "height": 480, + "sha256": "29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.151375075802207, + "total": 0.15174993313848972 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 143593, + "request_sha256": "644de408fef8e6cb532ef8b5144f8d87742837887e4f05984b55341187a23a5b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.079470+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.151375075802207, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1804581843316555, + "total": 0.18079206347465515 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336044342e-06, + "min": 2.7627744970784057e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091606234e-06, + "min": 9.860075820885874e-10 + }, + "position_error_m": { + "max": 1.0856805225155038e-07, + "min": 1.0856741727623932e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.2942989247886606 + }, + "yaw_error_rad": { + "max": 2.1965561156353097e-09, + "min": 2.196340269667258e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163122, + "request_sha256": "22c765474ea8183e3002b7f92eb27d13136b75d5e1515a007133afc05d459ec9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.082541+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1804581843316555, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 71367, + "format": "png", + "height": 480, + "sha256": "dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15029101632535458, + "total": 0.15058298595249653 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-dd863d7a52be1c7a4675c56c7154e2b572a5960be421b9f2a7ca27a7f2836348", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 154718, + "request_sha256": "f73d1ad36b4b6537aec4ac4c603a04db290f1a02263ed7b48f65de7f8641edc9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.085770+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15029101632535458, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80112, + "format": "png", + "height": 480, + "sha256": "441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1573751214891672, + "total": 0.15762518160045147 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163463, + "request_sha256": "14d0e74adc997a0af6a29feb0bb8b5c7180fe519679936fa90265f896a87a350", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.089228+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1573751214891672, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1619590912014246, + "total": 0.16233418136835098 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163219, + "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.092502+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1619590912014246, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80061, + "format": "png", + "height": 480, + "sha256": "83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17083296552300453, + "total": 0.17112493515014648 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163519, + "request_sha256": "b9b3db8ea54fbadede8971e74c99fd7fecb513696e6a57c86f43b8a392b15cee", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.096129+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17083296552300453, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81061, + "format": "png", + "height": 480, + "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16583316028118134, + "total": 0.1662911381572485 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164412, + "request_sha256": "0caffd3a26a3698840489fb4f7d7628a99d28dd969ab7e968b681dbf57380666", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.099757+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16583316028118134, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 87865, + "format": "png", + "height": 480, + "sha256": "2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17449981532990932, + "total": 0.1748749054968357 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-2679f2897c559ff07190c9bee25b0fbdb9f6cda29847c287cbadf74d342b4c14", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 0.003368756823804283, + "linear_speed_m_s": 0.00010986191904928065, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.0040346175968384084, + "min": 0.0024920357101732454 + }, + "linear_speed_m_s": { + "max": 0.00014294490050929373, + "min": 0.00010986191904928065 + }, + "position_error_m": { + "max": 0.0006551670719148867, + "min": 0.0006474789378581962 + }, + "robot_contact_force_n": { + "max": 0.7878949274134518, + "min": 0.7857892493442301 + }, + "table_support_force_n": { + "max": 0.2640607875145524, + "min": 0.25922471284709236 + }, + "yaw_error_rad": { + "max": 0.003720938606997237, + "min": 0.003703847249796388 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.0006551670719148867, + "robot_contact_force_n": 0.7863052928276089, + "table_support_force_n": 0.2639875277923961, + "target_position_m": [ + 0.38020236953882225, + -0.0693768704293019, + -9.617134314363833e-05 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.003703847249796388 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 170795, + "request_sha256": "8e094213a08d6ed860d1f868517b5a22417bb89f93029779f41f2d5974f02ad2", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.103393+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17449981532990932, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15645800158381462, + "total": 0.15670806169509888 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.3623192350084462, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159768, + "request_sha256": "626df301847db14cad7060405b1875996cef9c3f6802e820440fc5cc3f47ac22", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.107079+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15645800158381462, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72825, + "format": "png", + "height": 480, + "sha256": "b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16075000166893005, + "total": 0.16116700135171413 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 155653, + "request_sha256": "6680c0e5be92f50394933cd91f7fb856cedfc428d98983f245ba5719c920bb37", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.111048+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16075000166893005, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-unreleased", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 85815, + "format": "png", + "height": 480, + "sha256": "9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15600002370774746, + "total": 0.15624985098838806 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9357687d5747c98916e705103aebc6f9b6900f7aef5ed675ca6dd942a19a66e7", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 169166, + "request_sha256": "f6c0b2aa823c13f89f27aabcbb83816816e0f68cf026923a8d8916c170287a83", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.115004+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15600002370774746, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16045919619500637, + "total": 0.16095908358693123 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162173, + "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.119090+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16045919619500637, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 64921, + "format": "png", + "height": 480, + "sha256": "3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1646250020712614, + "total": 0.1650000922381878 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3397103762d94870c515de9294b6999ad2c2b1cfc570daa1320b5724bf556fa8", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361619074e-06, + "min": 2.7627747344185066e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091174393e-06, + "min": 9.86007602226177e-10 + }, + "position_error_m": { + "max": 1.0856805243386722e-07, + "min": 1.0856741745389504e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866115 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 149220, + "request_sha256": "f58a5417aaa6d0d5528b2c0c672e2fd6dfd9fb13080c382c080475529b938ca9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.123177+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1646250020712614, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 83583, + "format": "png", + "height": 480, + "sha256": "37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1566251739859581, + "total": 0.15700003132224083 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 166411, + "request_sha256": "fbdf1c3a54e1d8f3a51b5542528e4f621b5dd94e93db2228bc21828fbf8e4d85", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.127455+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1566251739859581, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81396, + "format": "png", + "height": 480, + "sha256": "266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15649991109967232, + "total": 0.15691597945988178 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163341, + "request_sha256": "3908d00ae374e96f852d1e026bfd79ba6c1b2457611d9ae362168ae2ab1fd5b1", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.131662+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15649991109967232, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1574999187141657, + "total": 0.15787500888109207 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 160407, + "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.136135+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1574999187141657, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16158400103449821, + "total": 0.1618748065084219 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.3624151236609657, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162934, + "request_sha256": "0b5063721c28b72b90cd69cf3ff5eccd3d6a9b4e1f4b6e04d4acdeba9c5813e1", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.140603+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16158400103449821, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78104, + "format": "png", + "height": 480, + "sha256": "3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14870893210172653, + "total": 0.14904187992215157 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159349, + "request_sha256": "e0143d52be87f1d72e4d955ea76e61e6fcdda047f9725766666cc6be808135ed", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.145099+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14870893210172653, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79100, + "format": "png", + "height": 480, + "sha256": "78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16175000928342342, + "total": 0.1620408147573471 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161675, + "request_sha256": "c48a75ef61b1c37da9f78fced2ad8c8fb64bb824764df2bafb810b5272ddb9b0", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.149715+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16175000928342342, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-moving_alias", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15062489546835423, + "total": 0.15095784328877926 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159007, + "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.154368+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15062489546835423, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_position", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80849, + "format": "png", + "height": 480, + "sha256": "a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.14754105359315872, + "total": 0.14783302322030067 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162094, + "request_sha256": "691dbe273c38c698069ed53d87cc9b4bce9c714f66fd76e11d89569a7be6de69", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.159028+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.14754105359315872, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79569, + "format": "png", + "height": 480, + "sha256": "66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1476658508181572, + "total": 0.14787493273615837 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66c3cc6f1378b262837d1b1d81c60d60bd9e3c1d25e59e28626810526d78b494", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161514, + "request_sha256": "fd64d5be5c9fefcc4cbb3c3b42e73bafb28bf0fe169af7cb0dc2b64ec7b0bc32", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.163763+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1476658508181572, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79111, + "format": "png", + "height": 480, + "sha256": "56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15441584400832653, + "total": 0.15474995598196983 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-56253438617f3ff5caf1700673122b6e7260601fad6700f519a8442f3e57bd37", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161939, + "request_sha256": "9aad7d221c1a2815360c5a7c7fc5d71ed186731fe9d727724868bedb5eb2042c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.168781+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15441584400832653, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-occluded", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 68346, + "format": "png", + "height": 480, + "sha256": "747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1557080540806055, + "total": 0.15600002370774746 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627744970784057e-09, + "linear_speed_m_s": 9.860075820885874e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336044342e-06, + "min": 2.7627744970784057e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091606234e-06, + "min": 9.860075820885874e-10 + }, + "position_error_m": { + "max": 1.0856805225155038e-07, + "min": 1.0856741727623932e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.2942989247886606 + }, + "yaw_error_rad": { + "max": 2.1965561156353097e-09, + "min": 2.196340269667258e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805225155038e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.3999999772538078, + -0.10000010615852632, + -0.00010773874568926898 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561156353097e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 152123, + "request_sha256": "207e6f5519077f996117540549c91f9b242b6811633219a3d98f3141966939c6", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.173751+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1557080540806055, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 70111, + "format": "png", + "height": 480, + "sha256": "9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.158791895955801, + "total": 0.15908293426036835 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151986, + "request_sha256": "38c739b8b55cb6da3677e531b81e3868e16ced493b42c708c8e027fb8a653b71", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.178809+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.158791895955801, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 76269, + "format": "png", + "height": 480, + "sha256": "a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16958406195044518, + "total": 0.16979197971522808 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627747672417554e-09, + "linear_speed_m_s": 9.86007598437561e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361383416e-06, + "min": 2.7627747672417554e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091079773e-06, + "min": 9.86007598437561e-10 + }, + "position_error_m": { + "max": 1.0856805194707237e-07, + "min": 1.0856741703541562e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 1.5707963245985561, + "min": 1.5707963245983396 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805194707237e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4200001083656286, + -0.13000000662664574, + -0.0001077387456892685 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.57079632459834 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 158446, + "request_sha256": "cc06c03336fd573f3f93300c40f09356b1e6e25ce2e0e7f4c6f6c2b7e5b9e0eb", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.184334+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16958406195044518, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_orientation", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 76269, + "format": "png", + "height": 480, + "sha256": "a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.15383283607661724, + "total": 0.1541248057037592 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a827a2f466b93f84ffc40ae11374a03bc7cbb222d194387bca4e1ee48d15887a", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 157514, + "request_sha256": "c7abe212b11813b00ca8d4dcea622a21d1aff59d03c4a29bb21feb10939d813b", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.189846+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.15383283607661724, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-unreleased", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 84090, + "format": "png", + "height": 480, + "sha256": "3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.20058383233845234, + "total": 0.20095892250537872 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3816b70bacff446c8901713b140fe6d615c880605cdd3ea89089495c641b77d2", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 0.003462491733548149, + "linear_speed_m_s": 9.969011014506973e-05, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 0.005988177262940627, + "min": 0.003462491733548149 + }, + "linear_speed_m_s": { + "max": 9.969011014506973e-05, + "min": 9.643102940812748e-05 + }, + "position_error_m": { + "max": 0.005753664112566862, + "min": 0.00574563428980446 + }, + "robot_contact_force_n": { + "max": 0.7619265340093823, + "min": 0.760598912244461 + }, + "table_support_force_n": { + "max": 0.30103792478353864, + "min": 0.2995624074490855 + }, + "yaw_error_rad": { + "max": 0.08440615838439973, + "min": 0.08437097640564 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.00574563428980446, + "robot_contact_force_n": 0.7619265340093823, + "table_support_force_n": 0.2995624074490855, + "target_position_m": [ + 0.40145485082066407, + -0.09444160792298799, + -0.00010962509298383693 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 0.08440615838439973 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167888, + "request_sha256": "27fd66094c3efe56842da714acc7be74fc97e09dea4ad67d7a4fe29ffa17d7a7", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.195262+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.20058383233845234, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79100, + "format": "png", + "height": 480, + "sha256": "78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.19983411766588688, + "total": 0.20112493075430393 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-78733a16c745bbd647514a3aa15b486f2b799cd413027728fd933b4a72954ab8", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 161045, + "request_sha256": "3650b3cd6be734b696e8157da729427b1856d68eb905bce765cff3556c099814", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.201022+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.19983411766588688, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1655002124607563, + "total": 0.16587506979703903 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163219, + "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.206717+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1655002124607563, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.2026660367846489, + "total": 0.20316592417657375 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 9.86007602226177e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361619074e-06, + "min": 2.7627747344185066e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091174393e-06, + "min": 9.86007602226177e-10 + }, + "position_error_m": { + "max": 1.0856805243386722e-07, + "min": 1.0856741745389504e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866115 + }, + "yaw_error_rad": { + "max": 2.1965563118397924e-09, + "min": 2.1963403873392906e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164167, + "request_sha256": "faa58fbc2678ab387f1f7f8b7c24aa781e0259cbf19a89a7459932ecb2a5d768", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.212481+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.2026660367846489, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 81396, + "format": "png", + "height": 480, + "sha256": "266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1894170418381691, + "total": 0.18974998965859413 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-266dcb71e61f8151fa2e56e7e3db4f561ff42830e44d0c7ff25cd7ea59843957", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "measured_state": { + "angular_speed_rad_s": 2.762774718512132e-09, + "linear_speed_m_s": 9.86007647555542e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361579335e-06, + "min": 2.762774718512132e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090868942e-06, + "min": 9.86007647555542e-10 + }, + "position_error_m": { + "max": 0.1699999688431964, + "min": 0.16999996884296226 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014348, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.19655620081749e-09, + "min": 2.1963403318281394e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.16999996884296226, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014348, + "target_position_m": [ + 0.5499999688429305, + -0.07000010400124504, + -0.00010773874568926812 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.19655620081749e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164275, + "request_sha256": "8a5b29305dec3510c2c7960557a0ef043eafb4237293f6b1507f3197242607fa", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.218753+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1894170418381691, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-missing_target", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72825, + "format": "png", + "height": 480, + "sha256": "b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1798751763999462, + "total": 0.1805000938475132 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 156283, + "request_sha256": "150e2666d6e63b662012e973a5ab1e6e614d3492b7f324cdf3e9de9a3f0f4c98", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.224752+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1798751763999462, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-occluded", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 68346, + "format": "png", + "height": 480, + "sha256": "747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1576668582856655, + "total": 0.15799980610609055 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-747b50dcdcb37d89c0a4a882fff12057e9062bd2c171154d1a6d408ef3e8af0d", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151174, + "request_sha256": "f653413e688093b5eec5d7b0597618d5946991f0921fb372650c7f562decfe95", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.230981+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1576668582856655, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78104, + "format": "png", + "height": 480, + "sha256": "3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.19812490791082382, + "total": 0.19870884716510773 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-3e75f36cfbb95243a844e72ef822049b103cdf547c446d6e8e5652abf499a236", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159979, + "request_sha256": "b364766ccfff769c100afd83646101a3b4660191294982804942390f264d1cc3", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.237321+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.19812490791082382, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 83583, + "format": "png", + "height": 480, + "sha256": "37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.2163329627364874, + "total": 0.21679187193512917 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-37862bb8c714993723236aa25d806a62229856edb4d17c489a02ea1fd777326a", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "measured_state": { + "angular_speed_rad_s": 2.7627747009029746e-09, + "linear_speed_m_s": 9.860075879684076e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233362534212e-06, + "min": 2.7627747009029746e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509173234e-06, + "min": 9.860075879684076e-10 + }, + "position_error_m": { + "max": 0.1699999772540272, + "min": 0.1699999772538407 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143505, + "min": 0.2942989247886605 + }, + "yaw_error_rad": { + "max": 2.1965561161782934e-09, + "min": 2.1963402702132865e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.1699999772538407, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143505, + "target_position_m": [ + 0.5699999772538076, + -0.10000010615852632, + -0.00010773874568926917 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965561161782934e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 167351, + "request_sha256": "a0ed0c8c2f72b936e34711c88f874179a2af286a17e8eaf9068dfe41bdf8e778", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.243762+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.2163329627364874, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 70111, + "format": "png", + "height": 480, + "sha256": "9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1909579150378704, + "total": 0.19158306531608105 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9d747de133583f8a3977332732a609f68eed97879615fdcee73ec0b12d091ecf", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 151356, + "request_sha256": "517d5035ec97f26d3d2c5ba307c827dac3f5e5a2cc831b82d648c94cdb6f2ef8", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.250598+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1909579150378704, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-missing_target", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 72934, + "format": "png", + "height": 480, + "sha256": "d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17470913007855415, + "total": 0.1752499956637621 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-d091c877c2762f156c115dc2a2d960d2619c4a47279dbf5bee136aaa8ace6d1d", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 154879, + "request_sha256": "4a5cfb98445b511272f4eece16db8e09eedb075da0ce456fedc68d1280dda3f6", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.257469+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17470913007855415, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-03-complete", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + }, + "goal": { + "bytes": 78462, + "format": "png", + "height": 480, + "sha256": "9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1829999964684248, + "total": 0.18366705626249313 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "goal_image": "dvidia-image-goal-9e077553701f0b54929386b69abc0d4a531f4dd8d0ef8c164dfbd4e11c2eec22", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 160407, + "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.264310+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1829999964684248, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-moving_alias", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18179207108914852, + "total": 0.18225004896521568 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.7627747344185066e-09, + "linear_speed_m_s": 0.12, + "native_observation_count": 0, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": null, + "min": null + }, + "table_support_force_n": { + "max": null, + "min": null + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.0, + "position_error_m": 1.0856805243386722e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.36239906646539655, + "target_position_m": [ + 0.4299999858101823, + -0.16000010763675526, + -0.00010773874568926887 + ], + "target_present": true, + "target_present_observation_count": 0, + "yaw_error_rad": 2.1965563118397924e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163981, + "request_sha256": "2b2cf534f89561d955526b1bb1db41baba75a591546dcd80349293a345ae7877", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.271046+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18179207108914852, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-wrong_position", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80849, + "format": "png", + "height": 480, + "sha256": "a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.18854206427931786, + "total": 0.18925010226666927 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-a000a38d4e067649cdcac56ec77942730e0a67093338880d974e90e912b7df48", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.762774742684285e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.0468233361281146e-06, + "min": 2.762774742684285e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925090883427e-06, + "min": 9.86007605782391e-10 + }, + "position_error_m": { + "max": 0.16999999337348212, + "min": 0.16999999337338886 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.29429999900143494, + "min": 0.2942989247886607 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 0.16999999337338886, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.29429999900143494, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 163033, + "request_sha256": "9a86c3e586b664e74763f17964bb1f41aae3dc2d3e938a2af44de193e52754d8", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.277869+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.18854206427931786, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-01-wrong_object", + "condition": "rgb", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80061, + "format": "png", + "height": 480, + "sha256": "83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "width": 640 + }, + "goal": { + "bytes": 79345, + "format": "png", + "height": 480, + "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.16075000166893005, + "total": 0.1612498890608549 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-83af097bb71ac714e02954029a91edc4fb8f51948e01ed70d31d705dcf593452", + "goal_image": "dvidia-image-goal-550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb", + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162889, + "request_sha256": "57238b47f9786cb04bcc447130e5b4be6db4fe108092005392ea161d36e7b8f9", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.284897+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.16075000166893005, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-04-complete", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + }, + "goal": { + "bytes": 77762, + "format": "png", + "height": 480, + "sha256": "76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17070886678993702, + "total": 0.17099990509450436 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "goal_image": "dvidia-image-goal-76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa", + "measured_state": { + "angular_speed_rad_s": 2.7627745907984584e-09, + "linear_speed_m_s": 9.860076016164131e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.046823336175752e-06, + "min": 2.7627745907984584e-09 + }, + "linear_speed_m_s": { + "max": 1.057792509081208e-06, + "min": 9.860076016164131e-10 + }, + "position_error_m": { + "max": 1.0856805211389336e-07, + "min": 1.085674170840469e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.294299999001435, + "min": 0.29429892478866077 + }, + "yaw_error_rad": { + "max": 2.1965565755177607e-09, + "min": 2.196340442850442e-09 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805211389336e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.294299999001435, + "target_position_m": [ + 0.4199999933733547, + -0.13000010836562884, + -0.00010773874568926879 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 2.1965565755177607e-09 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 159953, + "request_sha256": "63d8307d311a3bfd90ef515017c251ee82d3e4b714f7fd7bd716bf53b4d5974d", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.291842+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17070886678993702, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_object", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 80112, + "format": "png", + "height": 480, + "sha256": "441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.17170794308185577, + "total": 0.17200014553964138 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-441dadb181f982e7393eb40642a2a035037f88a13c170b9b227cd6e46f3f4ebc", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": null, + "linear_speed_m_s": null, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": null, + "min": null + }, + "linear_speed_m_s": { + "max": null, + "min": null + }, + "position_error_m": { + "max": null, + "min": null + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.0, + "min": 0.0 + }, + "yaw_error_rad": { + "max": null, + "min": null + } + }, + "observation_window_s": 0.2, + "position_error_m": null, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.0, + "target_position_m": null, + "target_present": false, + "target_present_observation_count": 0, + "yaw_error_rad": null + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 164093, + "request_sha256": "01b8450027128d7fe455f7d3b39c54fcac74a8fe25ff196aa0e15237b9f4f35f", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.298993+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.17170794308185577, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + }, + { + "attempts": 0, + "case_id": "layout-02-wrong_orientation", + "condition": "rgb_telemetry", + "decision": { + "complete": null, + "present": null + }, + "error": { + "kind": "missing_credentials", + "message": "TYPESAFE_API_KEY is not configured" + }, + "estimated_input_cost_usd": null, + "http_status": null, + "images": { + "current": { + "bytes": 78325, + "format": "png", + "height": 480, + "sha256": "66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "width": 640 + }, + "goal": { + "bytes": 79868, + "format": "png", + "height": 480, + "sha256": "a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "width": 640 + } + }, + "latency_ms": { + "http": null, + "prepare": 0.1871250569820404, + "total": 0.18762494437396526 + }, + "model": "jev-1.14.0", + "online_total_ms": null, + "p_complete": null, + "p_present": null, + "presence": "unknown", + "request": { + "assets": { + "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144": { + "format": "png", + "part": "current", + "type": "image" + }, + "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3": { + "format": "png", + "part": "goal", + "type": "image" + } + }, + "model": "jev-1.14.0", + "questions": { + "completion": { + "criteria": { + "false": "At least one physical requirement explicitly defined in `task` is not satisfied.", + "true": "All physical requirements explicitly defined in `task` are satisfied." + }, + "instructions": "Has the physical goal in `task` actually been achieved now? Compare `current_image` with `goal_image` and use any supplied `measured_state`. Matching appearance alone does not prove hidden support, release, stability, identity or dwell requirements. Return the probability of physical completion; express uncertainty when the provided evidence cannot establish those requirements.", + "type": "noul" + }, + "presence": { + "criteria": { + "false": "The target object is absent from the current scene.", + "true": "The target object is present in the current scene." + }, + "instructions": "Is the target object specified by `task` and shown in `goal_image` present in the current scene? Use `current_image` and any supplied `measured_state`. Do not confuse a different object with the target. Occlusion or missing measurements can leave presence uncertain.", + "type": "noul" + }, + "verdict": { + "criteria": { + "complete": "Available evidence establishes every physical requirement in `task`.", + "incomplete": "Available evidence establishes that at least one physical requirement in `task` is not satisfied.", + "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`." + }, + "instructions": "What conclusion about completion of `task` is justified by `current_image`, `goal_image` and any supplied `measured_state`? Assess only the supplied evidence. Do not invent unseen telemetry or assume that a single image establishes stability over time.", + "type": "choice" + } + }, + "state": { + "current_image": "dvidia-image-current-66935db963dd00862c83e0fc8af06939d0f017d7801a328870f0feb49e64a144", + "goal_image": "dvidia-image-goal-a3221e451c24b164f0348f2cb6517d2bd2589a444c3d8d71a9942a98dee0bdf3", + "measured_state": { + "angular_speed_rad_s": 2.762774938435085e-09, + "linear_speed_m_s": 9.86007540976695e-10, + "native_observation_count": 100, + "native_timestep_s": 0.002, + "native_window_extrema": { + "angular_speed_rad_s": { + "max": 2.04682333632664e-06, + "min": 2.762774938435085e-09 + }, + "linear_speed_m_s": { + "max": 1.0577925091088337e-06, + "min": 9.86007540976695e-10 + }, + "position_error_m": { + "max": 1.0856805246501234e-07, + "min": 1.085674174082394e-07 + }, + "robot_contact_force_n": { + "max": 0.0, + "min": 0.0 + }, + "table_support_force_n": { + "max": 0.2942999990014351, + "min": 0.29429892478866126 + }, + "yaw_error_rad": { + "max": 1.5707963245985526, + "min": 1.5707963245983363 + } + }, + "observation_window_s": 0.2, + "position_error_m": 1.0856805246501234e-07, + "robot_contact_force_n": 0.0, + "table_support_force_n": 0.2942999990014351, + "target_position_m": [ + 0.4300001076367553, + -0.16000001418981774, + -0.0001077387456892687 + ], + "target_present": true, + "target_present_observation_count": 100, + "yaw_error_rad": 1.5707963245983367 + }, + "task": "Place the green shoe inside the outlined goal zone, matching the goal image's position and orientation. Completion requires the target shoe to have table support force greater than 0.01 N, robot contact force at most 0.01 N, XY position error at most 25 mm, yaw error at most 15 degrees, linear speed at most 0.01 m/s and angular speed at most 0.05 rad/s, all continuously for at least 200 ms. The goal picture is a visual reference, not proof that these physical conditions hold in the current picture. Answer unknown when the inputs cannot establish the required physical conditions. An orange shoe is a different object. The opaque inspection cover can hide the target." + } + }, + "request_bytes": 162608, + "request_sha256": "b525b55a98301c6e1360cf7f82ec7a4215658fd7ba841fa75de19aa3ceea649e", + "response_sha256": null, + "socket_timeout_s": 20.0, + "started_at": "2026-10-19T03:32:08.306196+00:00", + "status": "not_run", + "timing_ms": { + "prepare_ms": 0.1871250569820404, + "request_ms": null, + "total_ms": null + }, + "usage": null, + "verdict": "unknown", + "verdict_confidence": null, + "verdict_probabilities": null + } + ], + "schema": "dvidia.image-goal-results/v1", + "status": "not_run", + "updated_at": "2026-10-19T03:32:08.310051+00:00" +} diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json new file mode 100644 index 0000000..7eff49c --- /dev/null +++ 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{ + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258817669346 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.0671512911816513 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444035604506 + } + ], + "instantaneous_conditions_met": false + }, + { + "target_present": true, + "target_position_m": [ + 0.5899999933733543, + -0.13000010836562884, + -0.00010773874568926881 + ], + "position_error_m": 0.16999999337338886, + "yaw_error_rad": 2.1965565755177607e-09, + "linear_speed_m_s": 9.86007605782391e-10, + "angular_speed_rad_s": 2.762774742684285e-09, + "table_support_force_n": 0.29429999900143494, + "robot_contact_force_n": 0.0, + "contacts": [ + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.08002167923065535 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07290258819177643 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.06715129120249083 + }, + { + "other_geom": "workstation_table_top", + "normal_force_n": 0.07422444037651231 + } + ], + "instantaneous_conditions_met": false + } + ], + "solver_warnings": 0, + "object_attachment_constraints_added": 0, + "model_equalities": 1, + "external_forces_applied": false +} diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py new file mode 100644 index 0000000..05130db --- /dev/null +++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 +"""Independently audit frozen image-goal evidence, without rendering or an API. + +The numerical predicate below deliberately does not call the simulator's +``observe``/``_measure`` functions or trust their stored completion flags. +It checks recorded native evidence, not a fresh simulation or hardware run. +""" +from __future__ import annotations + +import argparse +from collections import Counter +from hashlib import sha256 +import json +import math +import os +from pathlib import Path, PurePosixPath +import platform +import sys + +sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src")) +from dvidia_training.image_goal_scenes import CRITERIA, expected_labels # noqa: E402 + +SCHEMA = "dvidia.image-goal-native-validation/v1" +METRICS = ("position_error_m", "yaw_error_rad", "linear_speed_m_s", + "angular_speed_rad_s", "table_support_force_n", "robot_contact_force_n") + + +def require(condition, message): + if not condition: + raise ValueError(message) + + +def finite(value, label): + require(type(value) in (int, float) and math.isfinite(value), f"Invalid finite number: {label}") + return float(value) + + +def same_number(actual, expected, label): + if expected is None: + require(actual is None, f"Expected null: {label}") + else: + require(math.isclose(finite(actual,label), finite(expected,label), rel_tol=1e-10, abs_tol=1e-12), + f"Numeric mismatch: {label}") + + +def regular_file(root, name): + require(isinstance(name,str) and "\\" not in name, "Manifest path must be POSIX text") + relative=PurePosixPath(name) + require(not relative.is_absolute() and relative.as_posix()==name and + all(part not in ("", ".", "..") for part in relative.parts), "Unsafe manifest path") + path=root.joinpath(*relative.parts) + require(all(not p.is_symlink() for p in (path,*path.parents) if p==root or root in p.parents), + f"Symlink in fixture path: {name}") + require(path.is_file(), f"Missing regular fixture file: {name}") + return path + + +def load_json(path): + def reject(value): + raise ValueError(f"Non-finite JSON value in {path.name}: {value}") + return json.loads(path.read_text(encoding="utf-8"),parse_constant=reject) + + +def physical_predicate(row, criteria): + """Recompute from numbers, ignoring instantaneous_conditions_met labels.""" + require(type(row["target_present"]) is bool, "Presence must be a boolean") + if not row["target_present"]: + require(all(row[key] is None for key in METRICS[:4]), "Absent target has pose/speed evidence") + return False + values={key:finite(row[key],key) for key in METRICS} + require(all(value>=0 for value in values.values()), "Negative scalar physical measurement") + return (values["position_error_m"]<=criteria["position_tolerance_m"] + and values["yaw_error_rad"]<=criteria["yaw_tolerance_rad"] + and values["linear_speed_m_s"]<=criteria["linear_speed_max_m_s"] + and values["angular_speed_rad_s"]<=criteria["angular_speed_max_rad_s"] + and values["table_support_force_n"]>criteria["support_force_min_n"] + and values["robot_contact_force_n"]<=criteria["robot_force_max_n"]) + + +def recompute_truth(state, criteria, dt): + current=state["current_measurement"] + history=state["history"] + require(isinstance(history,list) and len(history)<=100, "Unexpected native observation window") + current_ok=physical_predicate(current,criteria) + count=0 + for row in reversed(history): + if not physical_predicate(row,criteria): + break + count+=1 + dwell=count*dt if current_ok else 0. + return {"present":current["target_present"], + "complete":bool(current_ok and dwell+1e-12>=criteria["continuous_dwell_s"]), + "physical_conditions_dwell_s":dwell, + "position_error_m":current["position_error_m"], + "yaw_error_rad":current["yaw_error_rad"], + "supported":finite(current["table_support_force_n"],"support")>criteria["support_force_min_n"], + "released":finite(current["robot_contact_force_n"],"robot contact")<=criteria["robot_force_max_n"], + "linear_speed_m_s":current["linear_speed_m_s"], + "angular_speed_rad_s":current["angular_speed_rad_s"]} + + +def compare_truth(stored, derived, label): + require(set(stored)==set(derived), f"Truth fields differ: {label}") + for key,value in derived.items(): + if type(value) is bool: + require(type(stored[key]) is bool and stored[key] is value, f"Truth mismatch: {label}.{key}") + else: + same_number(stored[key],value,f"{label}.{key}") + + +def verify_snapshot(state, layout): + """Cross-check raw native coordinates, independent of measurement labels.""" + measurement=state["current_measurement"] + for name in ("qpos","qvel","ctrl"): + require(isinstance(state[name],list), f"Invalid {name}") + for value in state[name]: + finite(value,name) + require(len(state["ctrl"])==7, "Expected seven pinned PiPER actuator commands") + if not measurement["target_present"]: + return + qpos,qvel=state["qpos"],state["qvel"] + require(len(qpos)==15 and len(qvel)==14, "Expected PiPER plus one free shoe") + for actual,recorded in zip(qpos[8:11],measurement["target_position_m"]): + same_number(actual,recorded,"free-body target position") + xy_error=math.hypot(qpos[8]-layout["xy_m"][0],qpos[9]-layout["xy_m"][1]) + same_number(xy_error,measurement["position_error_m"],"XY goal error") + w,x,y,z=qpos[11:15] + same_number(sum(v*v for v in (w,x,y,z)),1.,"unit free-body quaternion") + yaw=math.atan2(2*(w*z+x*y),1-2*(y*y+z*z)) + yaw_error=abs(math.atan2(math.sin(yaw-layout["yaw_rad"]),math.cos(yaw-layout["yaw_rad"]))) + same_number(yaw_error,measurement["yaw_error_rad"],"yaw goal error") + same_number(math.sqrt(sum(v*v for v in qvel[8:11])),measurement["linear_speed_m_s"],"native linear speed") + same_number(math.sqrt(sum(v*v for v in qvel[11:14])),measurement["angular_speed_rad_s"],"native angular speed") + + +def verify_telemetry(state, inputs, dt): + current,history=state["current_measurement"],state["history"] + require("complete" not in inputs and "instantaneous_conditions_met" not in inputs, + "A verdict leaked into measured inputs") + for key in ("target_present","target_position_m",*METRICS): + require(inputs[key]==current[key], f"Telemetry snapshot differs: {key}") + require(inputs["native_observation_count"]==len(history), "Native tick count differs") + require(inputs["target_present_observation_count"]==sum(row["target_present"] for row in history), + "Presence observation count differs") + same_number(inputs["native_timestep_s"],dt,"native dt") + same_number(inputs["observation_window_s"],len(history)*dt,"observation duration") + for key in METRICS: + values=[row[key] for row in history if row[key] is not None] + expected={"min":min(values) if values else None,"max":max(values) if values else None} + for statistic,value in expected.items(): + same_number(inputs["native_window_extrema"][key][statistic],value,f"{key}.{statistic}") + + +def verify(fixtures): + root=Path(fixtures).absolute() + require(root.is_dir() and not root.is_symlink(), "Fixtures must be a regular directory") + integrity=load_json(regular_file(root,"integrity.json")) + records=integrity["files"] + names=[record["path"] for record in records] + require(len(set(names))==len(names), "Duplicate integrity path") + actual={p.relative_to(root).as_posix() for p in root.rglob("*") if p.is_file()} + required=set(names)|{"integrity.json"} + require(required<=actual and actual-required<={"evaluation-plan.json"}, + "Integrity manifest coverage differs or unknown additional files exist") + additional_metadata=[] + for name in sorted(actual-required): + data=regular_file(root,name).read_bytes() + additional_metadata.append({"path":name,"bytes":len(data),"sha256":sha256(data).hexdigest(), + "scope":"Runner metadata added after fixture generation; fingerprinted here, not claimed to be covered by the original fixture integrity manifest."}) + byte_count=0 + for record in records: + path=regular_file(root,record["path"]) + data=path.read_bytes() + require(len(data)==record["bytes"], f"Byte count differs: {record['path']}") + require(sha256(data).hexdigest()==record["sha256"], f"Hash differs: {record['path']}") + byte_count+=len(data) + protocol_path=regular_file(root,"protocol.json") + protocol=load_json(protocol_path) + manifest=load_json(regular_file(root,"cases.json")) + protocol_digest=sha256(protocol_path.read_bytes()).hexdigest() + require(protocol_digest==regular_file(root,"protocol.sha256").read_text().strip()==manifest["protocol_sha256"], + "Protocol digests disagree") + require(protocol["criteria"]==manifest["criteria"]==CRITERIA,"Criteria differ from the frozen verifier contract") + criteria=protocol["criteria"] + dt=finite(protocol["physics_timestep_s"],"physics timestep") + same_number(dt,.002,"fixed native timestep") + layouts={row["id"]:row for row in protocol["layouts"]} + cases=manifest["cases"] + require(len(cases)==32 and {case["id"] for case in cases}==set(protocol["case_ids"]), + "Frozen case coverage differs") + category_counts=Counter(case["category"] for case in cases) + require(set(category_counts)==set(protocol["categories"]) and set(category_counts.values())=={4}, + "Unbalanced category coverage") + rows=[];states={} + for case in cases: + identifier=case["id"] + state=load_json(regular_file(root,case["state_file"])) + states[identifier]=state + truth=recompute_truth(state,criteria,dt) + compare_truth(state["physical_truth"],truth,identifier+".state") + compare_truth(case["physical_truth"],truth,identifier+".case") + verify_snapshot(state,layouts[case["layout_id"]]) + verify_telemetry(state,case["measured_inputs"],dt) + require(state["solver_warnings"]==0,"Native solver warning recorded") + require(state["object_attachment_constraints_added"]==0,"Object attachment recorded") + require(state["model_equalities"]==1,"Unexpected equality constraint count") + require(state["external_forces_applied"] is False,"Undeclared external force recorded") + for row in (state["current_measurement"],*state["history"]): + require(row["instantaneous_conditions_met"] is physical_predicate(row,criteria), + f"Stored instantaneous label disagrees with numbers: {identifier}") + labels=expected_labels(case["category"],truth) + for key in labels: + require(case[key]==labels[key], f"Observability labels differ: {identifier}") + visibility=case["visibility"] + if case["category"] in ("occluded","missing_target","wrong_object"): + require(visibility["visible_target_pixels"]==0 and visibility["target_box_xyxy_normalized"] is None, + "An invisible/absent target has a visible box") + else: + require(visibility["visible_target_pixels"]>0 and visibility["target_box_xyxy_normalized"] is not None, + "Visible target is missing its native segmentation box") + if case["category"]=="unreleased": + require(truth["supported"] and not truth["released"] and not truth["complete"], + "Unreleased fixture lacks supported robot contact") + require(truth["position_error_m"]<=criteria["position_tolerance_m"] and + truth["yaw_error_rad"]<=criteria["yaw_tolerance_rad"], + "Unreleased fixture is outside the goal pose") + require(case["expected_verdict"]=={"rgb":"unknown","rgb_telemetry":"incomplete"}, + "Unreleased RGB overclaims physical contact force") + rows.append({"id":identifier,"category":case["category"],"present":truth["present"], + "complete":truth["complete"],"physical_conditions_dwell_s":truth["physical_conditions_dwell_s"], + "supported":truth["supported"],"released":truth["released"], + "position_error_m":truth["position_error_m"],"yaw_error_rad":truth["yaw_error_rad"], + "table_support_force_n":state["current_measurement"]["table_support_force_n"], + "robot_contact_force_n":state["current_measurement"]["robot_contact_force_n"], + "linear_speed_m_s":truth["linear_speed_m_s"],"angular_speed_rad_s":truth["angular_speed_rad_s"], + "native_history_samples":len(state["history"]),"expected_verdict":labels["expected_verdict"]}) + index={case["id"]:case for case in cases} + aliases=[] + require(len(manifest["aliases"])==4,"Expected four exact-pixel aliases") + require({pair["case_id"] for pair in manifest["aliases"]}== + {case["id"] for case in cases if case["category"]=="moving_alias"}, + "Alias manifest does not cover all four moving controls") + for pair in manifest["aliases"]: + moving,stationary=index[pair["case_id"]],index[pair["pixel_identical_to"]] + require(moving["category"]=="moving_alias" and stationary["category"]=="complete" and + moving["layout_id"]==stationary["layout_id"],"Alias identity differs") + a=regular_file(root,moving["current_image"]).read_bytes() + b=regular_file(root,stationary["current_image"]).read_bytes() + require(a==b,"Current-image PNG alias bytes differ") + require(states[moving["id"]]["qpos"]==states[stationary["id"]]["qpos"],"Alias native poses differ") + require(states[moving["id"]]["qvel"]!=states[stationary["id"]]["qvel"],"Alias native velocities do not differ") + require(not moving["physical_truth"]["complete"] and stationary["physical_truth"]["complete"], + "Alias physical truth does not differ") + require(len(states[moving["id"]]["history"])==0,"Post-intervention observation time was invented") + aliases.append({**pair,"png_sha256":sha256(a).hexdigest(),"png_bytes":len(a), + "byte_identical":True,"pose_identical":True,"velocity_and_completion_differ":True}) + completed=sum(row["complete"] for row in rows) + require(completed==8 and len(rows)-completed==24,"Expected 8 complete and 24 incomplete states") + return {"schema":SCHEMA,"status":"passed", + "scope":"Independent recomputation from frozen native state measurements/history and raw coordinates. No images re-rendered, no fresh physics run, no model service called and no hardware validation.", + "fixture_generation_scope":manifest["scope"],"telemetry_scope":manifest["telemetry_scope"], + "source_digests":{"protocol_sha256":protocol_digest, + "cases_sha256":sha256(regular_file(root,"cases.json").read_bytes()).hexdigest(), + "integrity_sha256":sha256(regular_file(root,"integrity.json").read_bytes()).hexdigest(), + "verifier_sha256":sha256(Path(__file__).read_bytes()).hexdigest()}, + "files_verified":len(records),"bytes_verified":byte_count, + "additional_runner_metadata":additional_metadata, + "native_generation_wall_seconds":manifest["generation_wall_seconds"], + "native_generation_timing_scope":"Recorded fixture construction, stepping and rendering; excludes Jev inference and this verification.", + "recorded_mujoco_version":manifest["mujoco_version"], + "verification_host":{"system":platform.system(),"release":platform.release(), + "machine":platform.machine(),"processor":platform.processor() or None, + "logical_cpu_count":os.cpu_count(),"python_version":platform.python_version(), + "scope":"Host running this verifier; CPU model, RAM and GPU specifications are not inferred."}, + "criteria":criteria,"case_count":len(rows),"category_counts":dict(sorted(category_counts.items())), + "physical_complete":completed,"physical_incomplete":len(rows)-completed, + "zero_solver_warnings":True,"no_added_object_attachment_constraints":True, + "no_recorded_external_forces":True,"native_equalities_per_model":1, + "equality_scope":"Recorded upstream gripper coupling count; verified against frozen state metadata, without recompiling models.", + "all_physical_labels_recomputed":True,"all_telemetry_extrema_recomputed":True, + "unreleased_rgb_unknown_cases":4,"aliases":aliases,"cases":rows} + + +def main(argv=None): + parser=argparse.ArgumentParser(description=__doc__) + parser.add_argument("--fixtures",required=True,type=Path) + parser.add_argument("--output",required=True,type=Path) + args=parser.parse_args(argv) + require(args.output.absolute()!=args.fixtures.absolute() and args.fixtures.absolute() not in args.output.absolute().parents, + "Write validation outside the frozen fixture directory") + try: + result=verify(args.fixtures) + code=0 + except (ValueError,KeyError,TypeError,OSError,json.JSONDecodeError) as error: + result={"schema":SCHEMA,"status":"failed","error":str(error)} + code=1 + args.output.parent.mkdir(parents=True,exist_ok=True) + args.output.write_text(json.dumps(result,indent=2,allow_nan=False)+"\n",encoding="utf-8") + print(json.dumps({key:result[key] for key in ("status","case_count","physical_complete","physical_incomplete","files_verified","bytes_verified","error") if key in result})) + return code + + +if __name__=="__main__": + raise SystemExit(main())

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