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+# 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](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
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+++ 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
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+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md
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+# 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
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--- /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": {
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+        "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",
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+        "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"
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+  "cost_scope": "Planning allowance assumes documented roughly 64k default context. Not a billing guarantee; receipts retain known usage and unknown-cost attempts.",
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+      "sha256": "b1912487c8c421d3d0e6e5cc97a5f3b63f7a8625f441d5b26857955196f00e67"
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+      "sha256": "59a4c4c52f8ec8db8c9698d62dfedfc08c7cf106bda2446da852699f21e35cb0"
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+      "sha256": "78709db4cd96f5a1106cbd80a607b4a6ba91bfe587cdfa30becd5eafe5675ead"
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+      "sha256": "a4946f69e2ca43bafb326fd9d5df6255df23506902348af9df6b5785669153e9"
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+  "frozen_at": "2026-10-19T03:32:07.942824+00:00",
+  "latency_scope": "Preloaded image bytes + request construction + image upload + hosted response + parsing; excludes fixture file loading, prerecorded rendering, live camera, actuation and physical task duration.",
+  "max_requests": 64,
+  "model": "jev-1.14.0",
+  "planning_allowance_per_request_usd": 0.003,
+  "presence_thresholds": [
+    0.2,
+    0.8
+  ],
+  "price_checked_date": "2026-10-18",
+  "rules": [
+    "Keep every scheduled trial, including errors and unrun trials.",
+    "No retries, prompt tuning, threshold tuning or case selection after evaluation begins.",
+    "No direct actuator commands and no model training or distillation.",
+    "Report physical false completion separately from observability-aware accuracy.",
+    "No remote calls when credentials are absent; missing results are never scored as accurate."
+  ],
+  "schema": "dvidia.image-goal-evaluation-plan/v1",
+  "source_commit": "19acf0734d63ecebc07140c8d84302a54baee4c4",
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+      "sha256": "38fa1de5da6ee9f8e0ab60bc77d2561e82de0dad17dcc1d067b5816d8633996a"
+    },
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+      "sha256": "1fe41c3fe4d372b068681caf3f63766285b9238976043a9e15fe4dbadb40b79a"
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+      "sha256": "f5dad86c692144a1cf85bfeecf68b99318ea8d9df08e0fe5d47d144f80a380ee"
+    }
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+  "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.",
+  "timeout_s": 20.0,
+  "timeout_scope": "Per socket operation; not a hard wall-clock deadline.",
+  "trials": [
+    {
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+      "condition": "rgb_telemetry",
+      "request_sha256": "4b2d21390d91a0e27ed7498506c325fb33f997108f0cd688646df0c8767b06b5"
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+    },
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+      "request_sha256": "803f461156bfe7fc05d5fbbdab899f45c52b561e1700f4c8ba68873e3635d6dd"
+    },
+    {
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+      "request_sha256": "6e3c7bda63e2584be56d9f5ffbcdf6228ba3f44c1d45e4d53f708f7e873d12da"
+    },
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+      "request_sha256": "5427dbcbe70b437b4949c2ef974905d4710ea0c994e8d92fd207d1017d494d6d"
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+    },
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+    },
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+    },
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+    },
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+    },
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+      "request_sha256": "f653413e688093b5eec5d7b0597618d5946991f0921fb372650c7f562decfe95"
+    },
+    {
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+      "condition": "rgb_telemetry",
+      "request_sha256": "b364766ccfff769c100afd83646101a3b4660191294982804942390f264d1cc3"
+    },
+    {
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+    },
+    {
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+      "condition": "rgb_telemetry",
+      "request_sha256": "2b2cf534f89561d955526b1bb1db41baba75a591546dcd80349293a345ae7877"
+    },
+    {
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+      "condition": "rgb_telemetry",
+      "request_sha256": "9a86c3e586b664e74763f17964bb1f41aae3dc2d3e938a2af44de193e52754d8"
+    },
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+      "condition": "rgb",
+      "request_sha256": "57238b47f9786cb04bcc447130e5b4be6db4fe108092005392ea161d36e7b8f9"
+    },
+    {
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+      "request_sha256": "63d8307d311a3bfd90ef515017c251ee82d3e4b714f7fd7bd716bf53b4d5974d"
+    },
+    {
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+      "request_sha256": "01b8450027128d7fe455f7d3b39c54fcac74a8fe25ff196aa0e15237b9f4f35f"
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+    {
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+      "condition": "rgb_telemetry",
+      "request_sha256": "b525b55a98301c6e1360cf7f82ec7a4215658fd7ba841fa75de19aa3ceea649e"
+    }
+  ]
+}
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+</style></head><body><a class="skip" href="#experiment">Skip to experiment</a><header class="masthead"><a class="brand" href="https://dvidia.org/" aria-label="DVIDIA home"><svg viewBox="0 0 64 64" aria-hidden="true"><rect width="64" height="64" rx="16" fill="#3c6cff"/><path d="M16 0h32c8.8 0 16 7.2 16 16v5C45 8 25 8 0 32V16C0 7.2 7.2 0 16 0Z" fill="#fff" opacity=".16"/><g fill="#f7f6f2"><rect x="8" y="20" width="4.6" height="24" rx="1.2" opacity=".28"/><rect x="14.1" y="20" width="4.6" height="24" rx="1.2" opacity=".38"/><rect x="20.2" y="20" width="4.6" height="24" rx="1.2" opacity=".48"/><rect x="26.3" y="20" width="4.6" height="24" rx="1.2" opacity=".58"/><rect x="32.4" y="20" width="4.6" height="24" rx="1.2" opacity=".68"/><rect x="38.5" y="20" width="4.6" height="24" rx="1.2" opacity=".78"/><rect x="44.6" y="20" width="4.6" height="24" rx="1.2" opacity=".9"/><rect x="50.7" y="20" width="4.6" height="24" rx="1.2" fill="#fff"/></g></svg><span>DVIDIA</span></a><nav class="top-links" aria-label="External resources"><a href="https://github.com/Dvidia-Inference/dvidia-training">Source code ↗</a><a href="https://research.dvidia.org/">Research notes ↗</a></nav></header>
+<main id="experiment"><p class="eyebrow">Robotics research · Image goals / 0.1</p><h1>Does it only look finished?</h1><p class="intro">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.</p><div id="run-state" class="run-state" role="status">Loading the recorded experiment.</div><noscript><p class="notice">Enable JavaScript to inspect individual cases. The complete public report remains available as JSON below.</p></noscript><div class="toolbar"><div><label for="condition">Model input</label><select id="condition"></select></div><p class="meta" id="run-meta"></p></div><dl id="metrics" class="metrics"></dl>
+<div class="workspace"><aside class="case-nav" aria-label="Benchmark cases"><h2>Inspect every case</h2><label class="meta" for="filter">Show</label><select id="filter" class="filter"><option value="all">All cases</option><option value="absent">Missing target</option><option value="incomplete">Physically incomplete</option><option value="false_complete">False completion</option><option value="error">Request errors</option><option value="not_run">Not run</option></select><div id="case-list" class="case-list"></div></aside><section aria-labelledby="case-title"><div class="case-heading"><h2 id="case-title"></h2><span id="case-category" class="meta"></span></div><div class="image-pair"><figure><img id="current" alt="Current simulated scene"><figcaption>Current observation</figcaption></figure><figure><img id="goal" alt="Requested goal arrangement"><figcaption>Goal reference</figcaption></figure></div><p id="case-notice" class="notice"></p><div class="comparison"><section><h3>Physical reference</h3><dl id="truth" class="facts"></dl><p class="note">Recorded simulator labels. A single image may not reveal hidden contact or sustained support.</p></section><section><h3>Jev judgment</h3><dl id="prediction" class="facts"></dl><p class="note">A remote research judgment. This page sends no images, runs no model and commands no robot.</p></section></div></section></div>
+<section class="fine"><h2>Count the misses. Keep the clock honest.</h2><p>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.</p><div class="table-wrap"><table><thead><tr><th>Decision check</th><th>Correct</th><th>Denominator</th></tr></thead><tbody id="decisions"></tbody></table></div><div class="table-wrap"><table><thead><tr><th>Latency scope</th><th>Timed requests</th><th>p50</th><th>p95</th><th>p99</th><th>Maximum</th></tr></thead><tbody id="latency"></tbody></table></div><p id="cost" class="note"></p><p>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.</p><p>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.</p><div class="downloads"><a href="report.json" download>Public results · JSON</a><a href="report-integrity.json" download>File checksums</a><a href="https://docs.typesafe.ai/images">Jev image documentation ↗</a></div></section><footer><p>Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a><br>Original report code: MIT. External models retain their own terms.</p><p><a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Hugging Face</a> · <a href="https://x.com/iammrriver">@iammrriver</a></p></footer></main>
+<script id="public-data" type="application/json">{"schema": "dvidia.image-goal-public-report/v1", "status": "not_run", "model": "jev-1.14.0", "conditions": ["rgb", "rgb_telemetry"], "cases": [{"id": "layout-01-complete", "category": "complete", "current_image": "images/550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb.png", "goal_image": "images/550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb.png", "ground_truth": {"present": true, "complete": true}, "expected_verdict": {"rgb": "unknown", "rgb_telemetry": "complete"}, "expected_presence": {"rgb": "present", "rgb_telemetry": "present"}}, {"id": "layout-01-missing_target", "category": "missing_target", "current_image": "images/b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d.png", "goal_image": "images/550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb.png", "ground_truth": {"present": false, "complete": false}, "expected_verdict": {"rgb": "incomplete", "rgb_telemetry": 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{"present": true, "complete": false}, "expected_verdict": {"rgb": "unknown", "rgb_telemetry": "incomplete"}, "expected_presence": {"rgb": "present", "rgb_telemetry": "present"}}, {"id": "layout-04-occluded", "category": "occluded", "current_image": "images/29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106.png", "goal_image": "images/76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa.png", "ground_truth": {"present": true, "complete": true}, "expected_verdict": {"rgb": "unknown", "rgb_telemetry": "complete"}, "expected_presence": {"rgb": "unknown", "rgb_telemetry": "present"}}], "results": [{"case_id": "layout-02-wrong_position", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-wrong_orientation", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": 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{"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-moving_alias", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-missing_target", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-unreleased", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-complete", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": 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{"case_id": "layout-04-missing_target", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-wrong_orientation", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-wrong_orientation", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-unreleased", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": 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null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-wrong_object", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-complete", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-wrong_object", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-wrong_orientation", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, 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"attempted": 0, "responses": 0, "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."}</script><script>
+"use strict";
+const data=JSON.parse(document.getElementById("public-data").textContent),byId=id=>document.getElementById(id),human=value=>String(value).replaceAll("_"," ").replaceAll("-"," "),labelCondition=value=>({rgb:"Images only",rgb_telemetry:"Images + measured state"}[value]||human(value)),success=new Set(["ok","success","completed"]),pending=new Set(["pending","not_run","not-run","unrun","missing_api_key","missing_credentials"]);let selected=data.cases[0].id;
+const text=(id,value)=>{byId(id).textContent=value},bool=value=>value===true?"Yes":value===false?"No":"Unreported",prob=value=>value===null?"Unreported":(value*100).toFixed(1)+"%",ms=value=>value===null?"Unreported":value.toFixed(1)+" ms",rowFor=(id,condition)=>data.results.find(row=>row.case_id===id&&row.condition===condition),condition=()=>byId("condition").value;
+for(const value of data.conditions){const option=document.createElement("option");option.value=value;option.textContent=labelCondition(value);byId("condition").append(option)}
+function pairs(id,values){const node=byId(id);node.replaceChildren();for(const [key,value] of values){const dt=document.createElement("dt"),dd=document.createElement("dd");dt.textContent=key;dd.textContent=value;node.append(dt,dd)}}
+function metric(title,value,detail){const wrap=document.createElement("div");wrap.className="metric";const dt=document.createElement("dt"),dd=document.createElement("dd"),small=document.createElement("small");dt.textContent=title;dd.textContent=value;small.textContent=detail;wrap.append(dt,dd,small);return wrap}
+function renderSummary(){
+const summary=data.summary.find(row=>row.condition===condition()),a=summary.observability_aware_accuracy,f=summary.false_completion,p=summary.false_presence;
+byId("metrics").replaceChildren(
+metric("Observability-aware judgments",summary.attempted&&a.planned?`${a.correct} / ${a.planned}`:"Not measured",`${summary.responses} responses · ${summary.completion_abstentions} abstentions · ${summary.errors} errors · ${summary.not_run} not run`),
+metric("Physical false completion",summary.attempted&&f.planned_incomplete?`${f.count} / ${f.planned_incomplete}`:"Not measured",`${f.count}/${f.valid_incomplete} valid responses · ${f.answered_incomplete}/${f.planned_incomplete} explicit-decision coverage`),
+metric("False target presence",summary.attempted&&p.planned_absent?`${p.count} / ${p.planned_absent}`:"Not measured",`${p.count}/${p.valid_absent} valid responses · ${p.answered_absent}/${p.planned_absent} explicit-decision coverage`),
+metric("Response latency · p95",ms(summary.successful_response_latency_ms.p95),`${summary.successful_response_latency_ms.n} timed successful responses`));
+text("run-meta",`${data.model} · ${data.cases.length} synthetic cases · ${summary.attempted}/${summary.planned} attempted in this condition`);
+const total=data.summary.reduce((n,s)=>n+s.attempted,0);text("run-state",total?`Recorded benchmark · ${total} requests attempted. Every planned case remains visible, including errors and missing responses.`:"Jev has not been run. These are prepared simulation cases; model accuracy and service latency have not been measured.");
+const decisionBody=byId("decisions");decisionBody.replaceChildren();for(const [name,count,denominator,scope] of [["Known verdicts",summary.known_verdict_accuracy.correct,summary.known_verdict_accuracy.planned,"planned cases with an observable verdict"],["Justified abstention",summary.ambiguous_abstention.correct,summary.ambiguous_abstention.planned,"planned ambiguous cases"],["Selective physical agreement",summary.completion_accuracy.correct,summary.completion_accuracy.answered,"explicit answered decisions"]]){const tr=document.createElement("tr");for(const value of [name,summary.attempted&&denominator?`${count} / ${denominator}`:"Not measured",scope]){const td=document.createElement("td");td.textContent=value;tr.append(td)}decisionBody.append(tr)}
+text("cost",`Known-usage estimated input cost: ${summary.known_usage_estimated_input_cost_usd===null?"unreported":"$"+summary.known_usage_estimated_input_cost_usd.toFixed(6)}. ${summary.unknown_cost_attempts} attempted requests have unknown cost. This estimate is not a billing receipt.`);
+const tbody=byId("latency");tbody.replaceChildren();for(const [name,values] of [["All timed attempts",summary.all_attempt_latency_ms],["Successful responses",summary.successful_response_latency_ms]]){const tr=document.createElement("tr");for(const value of [name,values.n,ms(values.p50),ms(values.p95),ms(values.p99),ms(values.max)]){const td=document.createElement("td");td.textContent=value;tr.append(td)}tbody.append(tr)}}
+function visible(caseRow){const row=rowFor(caseRow.id,condition()),filter=byId("filter").value;return filter==="all"||filter==="absent"&&caseRow.ground_truth.present===false||filter==="incomplete"&&caseRow.ground_truth.complete===false||filter==="false_complete"&&caseRow.ground_truth.complete===false&&row&&success.has(row.status)&&row.decision.complete===true||filter==="error"&&row&&!success.has(row.status)&&!pending.has(row.status)||filter==="not_run"&&(!row||pending.has(row.status))}
+function renderCases(){const shown=data.cases.filter(visible),list=byId("case-list");list.replaceChildren();if(!shown.length){const empty=document.createElement("p");empty.className="empty";empty.textContent="No cases match this filter.";list.append(empty)}if(shown.length&&!shown.some(row=>row.id===selected))selected=shown[0].id;for(const caseRow of shown){const row=rowFor(caseRow.id,condition()),button=document.createElement("button"),small=document.createElement("small");button.type="button";button.setAttribute("aria-pressed",String(caseRow.id===selected));button.textContent=human(caseRow.id);small.textContent=row?human(row.status):"Not run";if(row&&!success.has(row.status)&&!pending.has(row.status))small.className="error";button.append(small);button.addEventListener("click",()=>{selected=caseRow.id;renderCases()});list.append(button)}renderCase()}
+function renderCase(){
+const current=data.cases.find(row=>row.id===selected),row=rowFor(current.id,condition()),expected=current.expected_verdict[condition()],measured=row&&success.has(row.status);
+text("case-title",human(current.id));text("case-category",human(current.category));byId("current").src=current.current_image;byId("goal").src=current.goal_image;byId("current").alt=`Current simulated scene for ${human(current.id)}`;byId("goal").alt=`Goal reference for ${human(current.id)}`;
+pairs("truth",[["Target present",bool(current.ground_truth.present)],["Physically complete",bool(current.ground_truth.complete)],["Expected from this input",expected?human(expected):"Unreported"]]);
+pairs("prediction",[["Request status",row?human(row.status):"Not run"],["Target present",measured?bool(row.decision.present):"Unmeasured"],["Completion verdict",measured&&row.verdict?human(row.verdict):"Unmeasured"],["Presence probability",measured?prob(row.p_present):"Unmeasured"],["Completion probability",measured?prob(row.p_complete):"Unmeasured"],["Total latency",row&&!pending.has(row.status)?ms(row.timing_ms.total_ms??null):"Unmeasured"]]);
+let message="No Jev result is recorded for this case and input condition.";
+if(row&&success.has(row.status)){
+if(expected)message=row.verdict===expected?"The model judgment agrees with the frozen expected verdict for this input.":"Judgment disagreement: the model did not return the frozen expected verdict for this input.";
+else message=row.decision.complete===null?"The model did not make an explicit completion decision.":current.ground_truth.complete===null?"No physical completion reference was reported.":row.decision.complete!==current.ground_truth.complete?"Completion disagreement: the model judgment does not match the physical reference.":"Completion judgment agrees with the physical reference for this case.";
+if(expected==="unknown")message+=" This image does not establish every physical requirement; unknown is the expected answer.";
+if(current.ground_truth.complete===false&&row.decision.complete===true)message+=" False completion: the physical task is incomplete.";
+if(current.ground_truth.present===false&&row.decision.present===true)message+=" The model reported a target that is absent.";
+}else if(row&&!pending.has(row.status))message="The request failed. This case remains in the planned cohort and error count.";
+text("case-notice",message)}
+byId("condition").addEventListener("change",()=>{renderSummary();renderCases()});byId("filter").addEventListener("change",renderCases);renderSummary();renderCases();
+</script></body></html>
\ No newline at end of file
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json
new file mode 100644
index 0000000..6f5448a
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json
@@ -0,0 +1,340 @@
+{
+  "schema": "dvidia.image-goal-scenes/v1-integrity",
+  "files": [
+    {
+      "path": "cases.json",
+      "bytes": 103511,
+      "sha256": "f8b1b205b16e9833df7bd7063e0572355794dcede49a024f5d73ae6f65bbb1aa"
+    },
+    {
+      "path": "images/layout-01-complete.png",
+      "bytes": 79345,
+      "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb"
+    },
+    {
+      "path": "images/layout-01-missing_target.png",
+      "bytes": 72825,
+      "sha256": "b956ae597b88d4b82b67b00d6ad6e41bb75eb184fd320d94e2fa76115857d43d"
+    },
+    {
+      "path": "images/layout-01-moving_alias.png",
+      "bytes": 79345,
+      "sha256": "550516b315cb198407b0ef3be997af97805c11fcfe6b587b1091affd8060e8fb"
+    },
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE 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": {
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+    "integrity_sha256": "e8b6bbe4f2928e85f6e88d16af7d719dc32b1a3b588ce9633805061528cbe682",
+    "verifier_sha256": "514be68c1dbd9ecb797f46615524f2bffa8654a5df3a62b90f4f8cc897414c08"
+  },
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+      "scope": "Runner metadata added after fixture generation; fingerprinted here, not claimed to be covered by the original fixture integrity manifest."
+    }
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+  "native_generation_timing_scope": "Recorded fixture construction, stepping and rendering; excludes Jev inference and this verification.",
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+  "verification_host": {
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+    "release": "25.5.0",
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+    "scope": "Host running this verifier; CPU model, RAM and GPU specifications are not inferred."
+  },
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+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-01-missing_target",
+      "category": "missing_target",
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+      "complete": false,
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+      "supported": false,
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+      "linear_speed_m_s": null,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-wrong_object",
+      "category": "wrong_object",
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+      "complete": false,
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+      "linear_speed_m_s": null,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-wrong_position",
+      "category": "wrong_position",
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+      "complete": false,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-wrong_orientation",
+      "category": "wrong_orientation",
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+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-unreleased",
+      "category": "unreleased",
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+      "released": false,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-moving_alias",
+      "category": "moving_alias",
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+      "native_history_samples": 0,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-occluded",
+      "category": "occluded",
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+      "complete": true,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-02-complete",
+      "category": "complete",
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+      "complete": true,
+      "physical_conditions_dwell_s": 0.2,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-02-missing_target",
+      "category": "missing_target",
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+      "complete": false,
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+      "released": true,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-02-wrong_object",
+      "category": "wrong_object",
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+      "complete": false,
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+      "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",
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+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "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,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-02-unreleased",
+      "category": "unreleased",
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+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-02-moving_alias",
+      "category": "moving_alias",
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+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "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,
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+      "released": true,
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+      "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,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-03-missing_target",
+      "category": "missing_target",
+      "present": false,
+      "complete": false,
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+      "supported": false,
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+      "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,
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+      "released": true,
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+      "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",
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+      "complete": false,
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+      "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,
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+      "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,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-03-moving_alias",
+      "category": "moving_alias",
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+      "native_history_samples": 0,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-03-occluded",
+      "category": "occluded",
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+      "complete": true,
+      "physical_conditions_dwell_s": 0.2,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-04-complete",
+      "category": "complete",
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+      "complete": true,
+      "physical_conditions_dwell_s": 0.2,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-04-missing_target",
+      "category": "missing_target",
+      "present": false,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "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-04-wrong_object",
+      "category": "wrong_object",
+      "present": false,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
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+      "linear_speed_m_s": null,
+      "angular_speed_rad_s": null,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-wrong_position",
+      "category": "wrong_position",
+      "present": true,
+      "complete": false,
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+      "supported": true,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-wrong_orientation",
+      "category": "wrong_orientation",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-unreleased",
+      "category": "unreleased",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": false,
+      "position_error_m": 0.0010491825564488413,
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+      "linear_speed_m_s": 0.00010064949189093267,
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-moving_alias",
+      "category": "moving_alias",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": true,
+      "position_error_m": 1.0856805211389336e-07,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.3623192350084462,
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+      "linear_speed_m_s": 0.12,
+      "angular_speed_rad_s": 2.7627745907984584e-09,
+      "native_history_samples": 0,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-occluded",
+      "category": "occluded",
+      "present": true,
+      "complete": true,
+      "physical_conditions_dwell_s": 0.2,
+      "supported": true,
+      "released": true,
+      "position_error_m": 1.0856805211389336e-07,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.294299999001435,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 9.860076016164131e-10,
+      "angular_speed_rad_s": 2.7627745907984584e-09,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    }
+  ]
+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json
new file mode 100644
index 0000000..2be4fb8
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json
@@ -0,0 +1,146 @@
+{
+  "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
+  },
+  "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
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new file mode 100644
index 0000000..cf578ea
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+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/provenance.json
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+{
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+  "hardware_validated": false,
+  "learned_policy": false,
+  "native_fixture_files_copied_without_modification": true,
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+    "sha256": "e586f8e8a9ea1fd42efaaf497a9527992250a62115bf914d59014f9366d9c3fc"
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+    "sha256": "514be68c1dbd9ecb797f46615524f2bffa8654a5df3a62b90f4f8cc897414c08"
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+  "provider_model": "jev-1.14.0",
+  "provider_status": "not_run",
+  "public_results_omissions": [
+    "results[].response"
+  ],
+  "publication_source_commit": "3d3c8f660f6f9544d6a58c654bab143dd1eddc00",
+  "publication_source_url": "https://github.com/Dvidia-Inference/dvidia-training/tree/3d3c8f660f6f9544d6a58c654bab143dd1eddc00",
+  "research_url": "https://research.dvidia.org/papers/image-goal-jev-v01/",
+  "schema": "dvidia.image-goal-publication-provenance/v1",
+  "source_commit": "19acf0734d63ecebc07140c8d84302a54baee4c4",
+  "source_commit_scope": "Frozen native fixture generator, Jev request/evaluation protocol and scoring.",
+  "source_url": "https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4",
+  "space_url": "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/publication-integrity.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json
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index 0000000..1ef33fa
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json
new file mode 100644
index 0000000..74d01bc
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json
new file mode 100644
index 0000000..1ab6105
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json
@@ -0,0 +1,1596 @@
+{
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+  "status": "not_run",
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+      "current_image": "images/76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa.png",
+      "goal_image": "images/76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa.png",
+      "ground_truth": {
+        "present": true,
+        "complete": false
+      },
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      },
+      "expected_presence": {
+        "rgb": "present",
+        "rgb_telemetry": "present"
+      }
+    },
+    {
+      "id": "layout-04-occluded",
+      "category": "occluded",
+      "current_image": "images/29772e10911231b9260ebe8b91a7c7f8f482324bd5002900affc445a0eeb8106.png",
+      "goal_image": "images/76cba333e8bdf56f1b9e6011247095507ede97697ef99ada4b47ea7d1c71d3fa.png",
+      "ground_truth": {
+        "present": true,
+        "complete": true
+      },
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      },
+      "expected_presence": {
+        "rgb": "unknown",
+        "rgb_telemetry": "present"
+      }
+    }
+  ],
+  "results": [
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+      "case_id": "layout-02-wrong_position",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
+      "decision": {
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+    },
+    {
+      "case_id": "layout-02-wrong_orientation",
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+      "status": "not_run",
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+      },
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+      "verdict": null,
+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-01-moving_alias",
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+      "status": "not_run",
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+      },
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+      "timing_ms": {},
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+    },
+    {
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+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-03-moving_alias",
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+      "status": "not_run",
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+      },
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-03-complete",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
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+      },
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+      "timing_ms": {},
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+    },
+    {
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+      "status": "not_run",
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+      },
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-04-complete",
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-01-complete",
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+      "status": "not_run",
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+    },
+    {
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-02-moving_alias",
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+    },
+    {
+      "case_id": "layout-01-wrong_object",
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+    },
+    {
+      "case_id": "layout-02-wrong_position",
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+    },
+    {
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+      "condition": "rgb_telemetry",
+      "status": "not_run",
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+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-04-moving_alias",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
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+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-01-missing_target",
+      "condition": "rgb",
+      "status": "not_run",
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+        "complete": null
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+      "verdict": null,
+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-02-unreleased",
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+      "status": "not_run",
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+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-01-complete",
+      "condition": "rgb",
+      "status": "not_run",
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+    },
+    {
+      "case_id": "layout-02-occluded",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
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+    },
+    {
+      "case_id": "layout-01-wrong_position",
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+      "status": "not_run",
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-03-moving_alias",
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+    },
+    {
+      "case_id": "layout-01-moving_alias",
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+    },
+    {
+      "case_id": "layout-04-wrong_object",
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+    },
+    {
+      "case_id": "layout-03-wrong_object",
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+    },
+    {
+      "case_id": "layout-04-moving_alias",
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+    },
+    {
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-01-wrong_orientation",
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+    },
+    {
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+    },
+    {
+      "case_id": "layout-04-missing_target",
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+    },
+    {
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+    },
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+    },
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+      "case_id": "layout-02-complete",
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+      "case_id": "layout-02-complete",
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+    },
+    {
+      "case_id": "layout-03-wrong_position",
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+    },
+    {
+      "case_id": "layout-01-missing_target",
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+    },
+    {
+      "case_id": "layout-01-occluded",
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+    },
+    {
+      "case_id": "layout-04-wrong_object",
+      "condition": "rgb_telemetry",
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+    },
+    {
+      "case_id": "layout-01-wrong_position",
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+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-04-missing_target",
+      "condition": "rgb",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-03-missing_target",
+      "condition": "rgb",
+      "status": "not_run",
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+        "present": null,
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+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-03-complete",
+      "condition": "rgb",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "timing_ms": {},
+      "estimated_input_cost_usd": null
+    },
+    {
+      "case_id": "layout-02-moving_alias",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "p_present": null,
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-04-wrong_position",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "p_present": null,
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-01-wrong_object",
+      "condition": "rgb",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-04-complete",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-02-wrong_object",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      },
+      "p_present": null,
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+      "timing_ms": {},
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+    },
+    {
+      "case_id": "layout-02-wrong_orientation",
+      "condition": "rgb_telemetry",
+      "status": "not_run",
+      "decision": {
+        "present": null,
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+      "timing_ms": {},
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+  "summary": [
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+        "answered": 0,
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+      "observability_aware_accuracy": {
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+      "known_verdict_accuracy": {
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+        "planned": 16
+      },
+      "ambiguous_abstention": {
+        "correct": 0,
+        "planned": 16
+      },
+      "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
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+      "successful_response_latency_ms": {
+        "n": 0,
+        "p50": null,
+        "p95": null,
+        "p99": null,
+        "max": null
+      }
+    },
+    {
+      "condition": "rgb_telemetry",
+      "planned": 32,
+      "attempted": 0,
+      "responses": 0,
+      "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,
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+        "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"
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+  "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": [
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+      "case_id": "layout-02-wrong_position",
+      "condition": "rgb_telemetry",
+      "decision": {
+        "complete": null,
+        "present": null
+      },
+      "error": {
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+        "message": "TYPESAFE_API_KEY is not configured"
+      },
+      "estimated_input_cost_usd": null,
+      "http_status": null,
+      "images": {
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+          "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76",
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+            "type": "image"
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+            "part": "goal",
+            "type": "image"
+          }
+        },
+        "model": "jev-1.14.0",
+        "questions": {
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+            "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"
+          }
+        },
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+        }
+      },
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+              "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"
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+      "condition": "rgb",
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+        "message": "TYPESAFE_API_KEY is not configured"
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+        "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"
+          }
+        },
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+              "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`."
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+            "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"
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+            "type": "noul"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+          "verdict": {
+            "criteria": {
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+              "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"
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+            "type": "choice"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "type": "choice"
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+            "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"
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+              "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`."
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+            "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.",
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+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+          "presence": {
+            "criteria": {
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+              "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"
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+          "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"
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+            "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"
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+          "presence": {
+            "criteria": {
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+              "true": "The target object is present in the current scene."
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+            "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"
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+          "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`."
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "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"
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+              "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`."
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+            "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.",
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+            "type": "noul"
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+            "type": "noul"
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+              "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`."
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+            "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.",
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+              "false": "The target object is absent from the current scene.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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"
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "true": "The target object is present in the current scene."
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+            "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"
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+          "verdict": {
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+              "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"
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+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "type": "noul"
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+              "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`."
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+            "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"
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+            "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"
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+            "type": "choice"
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+            "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"
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+              "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"
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+            "type": "noul"
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+              "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`."
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+            "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.",
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+              "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`."
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+            "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"
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+          "presence": {
+            "criteria": {
+              "false": "The target object is absent from the current scene.",
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+            "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"
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+            "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`."
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+            "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"
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+              "false": "The target object is absent from the current scene.",
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+            "type": "noul"
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+            "criteria": {
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+              "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`."
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+            "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.",
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+            "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.",
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+            "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"
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+              "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"
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+            "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"
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+              "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`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+          "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"
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+          "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`."
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+            "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.",
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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"
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+            "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."
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+            "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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"
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+            "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."
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+            "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": {
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+              "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.",
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+  "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
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json
new file mode 100644
index 0000000..d671eba
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json
new file mode 100644
index 0000000..7eff49c
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json
new file mode 100644
index 0000000..6fd7a5e
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json
new file mode 100644
index 0000000..29b5cc1
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json
@@ -0,0 +1,1274 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json
new file mode 100644
index 0000000..0341181
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json
new file mode 100644
index 0000000..967c6bf
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json
new file mode 100644
index 0000000..07d6583
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json
new file mode 100644
index 0000000..7e3f3c3
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json
new file mode 100644
index 0000000..07d6583
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json
new file mode 100644
index 0000000..226b7ed
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json
new file mode 100644
index 0000000..d0e3226
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json
@@ -0,0 +1,1274 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json
new file mode 100644
index 0000000..dbb96fd
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json
new file mode 100644
index 0000000..5d2daf1
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json
new file mode 100644
index 0000000..1e9a062
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json
new file mode 100644
index 0000000..e0cb093
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json
new file mode 100644
index 0000000..1e9a062
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json
new file mode 100644
index 0000000..bec6399
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json
new file mode 100644
index 0000000..d14b9fc
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json
@@ -0,0 +1,1274 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json
new file mode 100644
index 0000000..d7a4ba9
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json
new file mode 100644
index 0000000..dfbd2ab
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json
new file mode 100644
index 0000000..e6ab29c
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json
@@ -0,0 +1,3395 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json
new file mode 100644
index 0000000..ee6f40e
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json
new file mode 100644
index 0000000..e6ab29c
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json
new file mode 100644
index 0000000..3f884f9
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json
new file mode 100644
index 0000000..9802fd3
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json
@@ -0,0 +1,1274 @@
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diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json
new file mode 100644
index 0000000..5c3b39e
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json
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+}
diff --git a/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json
new file mode 100644
index 0000000..d820b7f
--- /dev/null
+++ b/docs/downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json
@@ -0,0 +1,3395 @@
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+          "normal_force_n": 0.07422444000424769
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.1300001083656281,
+        -0.00010773877731637045
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.1965562424508533e-09,
+      "linear_speed_m_s": 2.140626236513871e-09,
+      "angular_speed_rad_s": 5.8188532633378e-09,
+      "table_support_force_n": 0.2942999978312484,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167896407635
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07290258794899619
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.0671512908695828
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07422444004859304
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.13000010836562817,
+        -0.00010773877332644568
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.196556270206429e-09,
+      "linear_speed_m_s": 1.994962390905455e-09,
+      "angular_speed_rad_s": 5.4383025933221595e-09,
+      "table_support_force_n": 0.29429999797889905,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167899780628
+        },
+        {
+          "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": [
+        0.5899999933733543,
+        -0.13000010836562842,
+        -0.0001077387629130402
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.196556408984307e-09,
+      "linear_speed_m_s": 1.614793822313443e-09,
+      "angular_speed_rad_s": 4.439143082588489e-09,
+      "table_support_force_n": 0.29429999836422666,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167908572033
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07290258805976647
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.06715129102088738
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07422444019785247
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.1300001083656285,
+        -0.00010773875990321382
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.1965564367398827e-09,
+      "linear_speed_m_s": 1.5049131944160074e-09,
+      "angular_speed_rad_s": 4.1485589987593594e-09,
+      "table_support_force_n": 0.29429999847558985,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167911109465
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07290258808287517
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.06715129105256243
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07422444022905758
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.13000010836562856,
+        -0.00010773875709819398
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.1965564367398827e-09,
+      "linear_speed_m_s": 1.4025099190536022e-09,
+      "angular_speed_rad_s": 3.876935729646871e-09,
+      "table_support_force_n": 0.29429999857937106,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.0800216791347261
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.0729025881043974
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.06715129108210273
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07422444025814484
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.1300001083656286,
+        -0.00010773875448404371
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.196556492251034e-09,
+      "linear_speed_m_s": 1.3070751376987822e-09,
+      "angular_speed_rad_s": 3.6230405882734287e-09,
+      "table_support_force_n": 0.2942999986760867,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167915673436
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07290258812444228
+        },
+        {
+          "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 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DVIDIA Research</title><link>https://research.dvidia.org/</link><description>Research footprints on demonstrations, calibrated simulation and robot skills.</description><language>en</language><atom:link href="https://research.dvidia.org/feed.xml" rel="self" type="application/rss+xml"/><item><title>A visual goal still needs physical feedback</title><link>https://research.dvidia.org/papers/goal-images-physical-feedback/</link><guid isPermaLink="true">https://research.dvidia.org/papers/goal-images-physical-feedback/</guid><pubDate>Sun, 18 Oct 2026 12:00:00 GMT</pubDate><description>14/14 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.</description></item><item><title>Give the gripper a fair test</title><link>https://research.dvidia.org/papers/shoe-graspability-v03/</link><guid isPermaLink="true">https://research.dvidia.org/papers/shoe-graspability-v03/</guid><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><description>24/24 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.</description></item><item><title>From reaching a shoe to keeping hold</title><link>https://research.dvidia.org/papers/continuous-shoe-grip-v02/</link><guid isPermaLink="true">https://research.dvidia.org/papers/continuous-shoe-grip-v02/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>20/20 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.</description></item><item><title>Skill prerequisites: access, reach and recovery</title><link>https://research.dvidia.org/papers/skill-prerequisites-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skill-prerequisites-v01/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>16/16 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.</description></item><item><title>Live PiPER shoe workstation v0.1</title><link>https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>Live 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.</description></item><item><title>Reusable shoe asset library v0.1</title><link>https://research.dvidia.org/papers/shoe-asset-library-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/shoe-asset-library-v01/</guid><pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate><description>Six 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.</description></item><item><title>PiPER placement reliability v0.5</title><link>https://research.dvidia.org/papers/piper-reliability-v05/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-reliability-v05/</guid><pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate><description>23/36 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.</description></item><item><title>PiPER contact recovery v0.4</title><link>https://research.dvidia.org/papers/piper-contact-recovery-v04/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-contact-recovery-v04/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>All 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.</description></item><item><title>PiPER contact history v0.3</title><link>https://research.dvidia.org/papers/piper-contact-history-v03/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-contact-history-v03/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>History 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.</description></item><item><title>PiPER grip validation and recovery</title><link>https://research.dvidia.org/papers/piper-grip-validation-and-recovery/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-grip-validation-and-recovery/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Moderate-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.</description></item><item><title>Native grasp task benchmark</title><link>https://research.dvidia.org/papers/native-grasp-task-benchmark/</link><guid isPermaLink="true">https://research.dvidia.org/papers/native-grasp-task-benchmark/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>6/6 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.</description></item><item><title>Hardware matched robot simulation</title><link>https://research.dvidia.org/papers/hardware-matched-robot-simulation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/hardware-matched-robot-simulation/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Pinned 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.</description></item><item><title>DVIDIA Observation Lab pilot</title><link>https://research.dvidia.org/papers/dvidia-observation-lab/</link><guid isPermaLink="true">https://research.dvidia.org/papers/dvidia-observation-lab/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Local 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.</description></item><item><title>Robotics research pipeline</title><link>https://research.dvidia.org/papers/robotics-research-pipeline/</link><guid isPermaLink="true">https://research.dvidia.org/papers/robotics-research-pipeline/</guid><pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate><description>Architecture 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.</description></item><item><title>Skillspace footage training pipeline</title><link>https://research.dvidia.org/papers/skillspace-footage-training/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-footage-training/</guid><pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate><description>Public 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&#39;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.</description></item><item><title>Skill capsule acquisition and optimization</title><link>https://research.dvidia.org/papers/skill-capsule-acquisition/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skill-capsule-acquisition/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Simulation-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.</description></item><item><title>Skillspace arm benchmarks and evolution</title><link>https://research.dvidia.org/papers/skillspace-arm-benchmarks/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-arm-benchmarks/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Mixed 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.</description></item><item><title>Installing a Skillspace on a simulated arm</title><link>https://research.dvidia.org/papers/skillspace-arm-installation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-arm-installation/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Public 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.</description></item><item><title>Offline cable training environment</title><link>https://research.dvidia.org/papers/offline-cable-training-environment/</link><guid isPermaLink="true">https://research.dvidia.org/papers/offline-cable-training-environment/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>CPU 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.</description></item><item><title>Fast accurate robot training simulation</title><link>https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>54 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.</description></item><item><title>Grok robotics research on X</title><link>https://research.dvidia.org/papers/grok-robotics-research-on-x/</link><guid isPermaLink="true">https://research.dvidia.org/papers/grok-robotics-research-on-x/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Primary 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.</description></item><item><title>Spatially grounded downloadable robot skills</title><link>https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/</link><guid isPermaLink="true">https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Source 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.</description></item><item><title>Clef and grounded video observations</title><link>https://research.dvidia.org/papers/clef-and-grounded-video-observations/</link><guid isPermaLink="true">https://research.dvidia.org/papers/clef-and-grounded-video-observations/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Five 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.</description></item><item><title>From Skillspace to robot skill</title><link>https://research.dvidia.org/papers/from-skillspace-to-robot-skill/</link><guid isPermaLink="true">https://research.dvidia.org/papers/from-skillspace-to-robot-skill/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Graduation 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.</description></item><item><title>Turn physical footage into testable evidence</title><link>https://research.dvidia.org/papers/physical-skill-research-agenda/</link><guid isPermaLink="true">https://research.dvidia.org/papers/physical-skill-research-agenda/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Four 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.</description></item></channel></rss>
+<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DVIDIA Research</title><link>https://research.dvidia.org/</link><description>Research footprints on demonstrations, calibrated simulation and robot skills.</description><language>en</language><atom:link href="https://research.dvidia.org/feed.xml" rel="self" type="application/rss+xml"/><item><title>Image-goal verification: native benchmark and Jev protocol</title><link>https://research.dvidia.org/papers/image-goal-jev-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/image-goal-jev-v01/</guid><pubDate>Sun, 18 Oct 2026 12:00:00 GMT</pubDate><description>32 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.</description></item><item><title>A visual goal still needs physical feedback</title><link>https://research.dvidia.org/papers/goal-images-physical-feedback/</link><guid isPermaLink="true">https://research.dvidia.org/papers/goal-images-physical-feedback/</guid><pubDate>Sun, 18 Oct 2026 12:00:00 GMT</pubDate><description>14/14 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.</description></item><item><title>Give the gripper a fair test</title><link>https://research.dvidia.org/papers/shoe-graspability-v03/</link><guid isPermaLink="true">https://research.dvidia.org/papers/shoe-graspability-v03/</guid><pubDate>Wed, 14 Oct 2026 12:00:00 GMT</pubDate><description>24/24 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.</description></item><item><title>From reaching a shoe to keeping hold</title><link>https://research.dvidia.org/papers/continuous-shoe-grip-v02/</link><guid isPermaLink="true">https://research.dvidia.org/papers/continuous-shoe-grip-v02/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>20/20 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.</description></item><item><title>Skill prerequisites: access, reach and recovery</title><link>https://research.dvidia.org/papers/skill-prerequisites-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skill-prerequisites-v01/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>16/16 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.</description></item><item><title>Live PiPER shoe workstation v0.1</title><link>https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/live-piper-shoe-workstation-v01/</guid><pubDate>Tue, 13 Oct 2026 12:00:00 GMT</pubDate><description>Live 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.</description></item><item><title>Reusable shoe asset library v0.1</title><link>https://research.dvidia.org/papers/shoe-asset-library-v01/</link><guid isPermaLink="true">https://research.dvidia.org/papers/shoe-asset-library-v01/</guid><pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate><description>Six 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.</description></item><item><title>PiPER placement reliability v0.5</title><link>https://research.dvidia.org/papers/piper-reliability-v05/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-reliability-v05/</guid><pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate><description>23/36 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.</description></item><item><title>PiPER contact recovery v0.4</title><link>https://research.dvidia.org/papers/piper-contact-recovery-v04/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-contact-recovery-v04/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>All 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.</description></item><item><title>PiPER contact history v0.3</title><link>https://research.dvidia.org/papers/piper-contact-history-v03/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-contact-history-v03/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>History 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.</description></item><item><title>PiPER grip validation and recovery</title><link>https://research.dvidia.org/papers/piper-grip-validation-and-recovery/</link><guid isPermaLink="true">https://research.dvidia.org/papers/piper-grip-validation-and-recovery/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Moderate-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.</description></item><item><title>Native grasp task benchmark</title><link>https://research.dvidia.org/papers/native-grasp-task-benchmark/</link><guid isPermaLink="true">https://research.dvidia.org/papers/native-grasp-task-benchmark/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>6/6 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.</description></item><item><title>Hardware matched robot simulation</title><link>https://research.dvidia.org/papers/hardware-matched-robot-simulation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/hardware-matched-robot-simulation/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Pinned 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.</description></item><item><title>DVIDIA Observation Lab pilot</title><link>https://research.dvidia.org/papers/dvidia-observation-lab/</link><guid isPermaLink="true">https://research.dvidia.org/papers/dvidia-observation-lab/</guid><pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate><description>Local 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.</description></item><item><title>Robotics research pipeline</title><link>https://research.dvidia.org/papers/robotics-research-pipeline/</link><guid isPermaLink="true">https://research.dvidia.org/papers/robotics-research-pipeline/</guid><pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate><description>Architecture 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.</description></item><item><title>Skillspace footage training pipeline</title><link>https://research.dvidia.org/papers/skillspace-footage-training/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-footage-training/</guid><pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate><description>Public 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&#39;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.</description></item><item><title>Skill capsule acquisition and optimization</title><link>https://research.dvidia.org/papers/skill-capsule-acquisition/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skill-capsule-acquisition/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Simulation-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.</description></item><item><title>Skillspace arm benchmarks and evolution</title><link>https://research.dvidia.org/papers/skillspace-arm-benchmarks/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-arm-benchmarks/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Mixed 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.</description></item><item><title>Installing a Skillspace on a simulated arm</title><link>https://research.dvidia.org/papers/skillspace-arm-installation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/skillspace-arm-installation/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Public 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.</description></item><item><title>Offline cable training environment</title><link>https://research.dvidia.org/papers/offline-cable-training-environment/</link><guid isPermaLink="true">https://research.dvidia.org/papers/offline-cable-training-environment/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>CPU 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.</description></item><item><title>Fast accurate robot training simulation</title><link>https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/</link><guid isPermaLink="true">https://research.dvidia.org/papers/fast-accurate-robot-training-simulation/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>54 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.</description></item><item><title>Grok robotics research on X</title><link>https://research.dvidia.org/papers/grok-robotics-research-on-x/</link><guid isPermaLink="true">https://research.dvidia.org/papers/grok-robotics-research-on-x/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Primary 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.</description></item><item><title>Spatially grounded downloadable robot skills</title><link>https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/</link><guid isPermaLink="true">https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/</guid><pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate><description>Source 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.</description></item><item><title>Clef and grounded video observations</title><link>https://research.dvidia.org/papers/clef-and-grounded-video-observations/</link><guid isPermaLink="true">https://research.dvidia.org/papers/clef-and-grounded-video-observations/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Five 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.</description></item><item><title>From Skillspace to robot skill</title><link>https://research.dvidia.org/papers/from-skillspace-to-robot-skill/</link><guid isPermaLink="true">https://research.dvidia.org/papers/from-skillspace-to-robot-skill/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Graduation 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.</description></item><item><title>Turn physical footage into testable evidence</title><link>https://research.dvidia.org/papers/physical-skill-research-agenda/</link><guid isPermaLink="true">https://research.dvidia.org/papers/physical-skill-research-agenda/</guid><pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate><description>Four 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.</description></item></channel></rss>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Research journal · DVIDIA Research</title><meta name="description" content="An open notebook on demonstrations, physical grounding, calibrated simulation and qualified robot skills."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="feed.xml"><link rel="icon" href="assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"CollectionPage","headline":"Research journal","description":"An open notebook on demonstrations, physical grounding, calibrated simulation and qualified robot skills.","url":"https://research.dvidia.org/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"}}</script><script src="app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="index.html" aria-current="page">Research journal</a><a href="index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="topics/capture-quality/index.html">Capture quality</a><a href="topics/skill-relations/index.html">Skill relations</a><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="index.html" aria-current="page">Research journal</a><a href="index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="topics/capture-quality/index.html">Capture quality</a><a href="topics/skill-relations/index.html">Skill relations</a><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><header class="journal-header"><p class="eyebrow">An open research notebook</p><h1>From observation<br>to physical skill.</h1><p class="journal-intro">We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.</p><p class="paper-meta">Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p></header><section class="journal-publications" aria-labelledby="publications"><div class="section-heading"><h2 id="publications">Publications</h2><p>25 papers and working notes</p></div><div data-paper-search hidden><label for="paper-search">Find a publication</label><input id="paper-search" type="search" autocomplete="off" placeholder="Search titles, questions and findings"><span data-search-count aria-live="polite"></span></div><div class="paper-list"><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-18">18 October 2026</time> · Research direction and passive native contact audit</p><h2><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></h2><p>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.</p><p class="paper-status">14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-14">14 October 2026</time> · Frozen shape, load and attached-tool diagnostic</p><h2><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></h2><p>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.</p><p class="paper-status">24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Frozen native comparison with independent contact audit</p><h2><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></h2><p>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.</p><p class="paper-status">20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Two native environments with authored policies and recorded benchmark evidence</p><h2><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></h2><p>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.</p><p class="paper-status">16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Live browser-native simulation with authored control and separate benchmark evidence</p><h2><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></h2><p>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.</p><p class="paper-status">Live browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · Versioned native scene assets and frozen passive-contact benchmark</p><h2><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></h2><p>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.</p><p class="paper-status">Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · Frozen paired reliability experiment with complete native evidence</p><h2><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></h2><p>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.</p><p class="paper-status">23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt</p><h2><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></h2><p>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.</p><p class="paper-status">All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired probe-only and contact-history experiment with complete confirmation receipt</p><h2><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></h2><p>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.</p><p class="paper-status">History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired authored controller experiment and complete confirmation receipt</p><h2><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></h2><p>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.</p><p class="paper-status">Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Native contact task implementation and authored baseline</p><h2><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></h2><p>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.</p><p class="paper-status">6/6 simulated placements · controls 0/12 · real-time and hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Pinned mechanics implementation and CPU numerical benchmark</p><h2><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></h2><p>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.</p><p class="paper-status">Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Local observation workflow and engineering feasibility pilot</p><h2><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></h2><p>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.</p><p class="paper-status">Local review release · motion and actuator source pilots · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-09">9 October 2026</time> · Architecture proposal and staged validation roadmap</p><h2><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></h2><p>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.</p><p class="paper-status">Architecture proposal · current simulation alpha · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-08">8 October 2026</time> · Product implementation and measured synthetic pilot</p><h2><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></h2><p>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&#39;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.</p><p class="paper-status">Public CPU package · one native scene qualified · simulation-only</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Learned movement artifact and installation prototype</p><h2><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></h2><p>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.</p><p class="paper-status">Simulation-only · teacher/student 7/8 · no overall speedup</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Paired simulation benchmark and product assessment</p><h2><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></h2><p>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.</p><p class="paper-status">Mixed held-out result · 30/32 baseline, 29/32 updated</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Product prototype and native contact qualification</p><h2><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></h2><p>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.</p><p class="paper-status">Public skill link to authored adapter · 6/6 scene placements</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Product prototype and local qualification</p><h2><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a></h2><p>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.</p><p class="paper-status">CPU simulation-only · Controller search and frozen evaluation executed</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Decision report and local experiment</p><h2><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></h2><p>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.</p><p class="paper-status">54 CPU fixture trials · GPU and physical transfer untested</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Source review and thesis comparison</p><h2><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></h2><p>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.</p><p class="paper-status">Primary sources checked · Experiments proposed</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Research synthesis and proposed experiments</p><h2><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></h2><p>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.</p><p class="paper-status">Source review · DVIDIA experiments not run</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Engineering feasibility pilot</p><h2><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></h2><p>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.</p><p class="paper-status">Five licensed excerpts · Accuracy evaluation outstanding</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Source review and implementation proposal</p><h2><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></h2><p>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.</p><p class="paper-status">Graduation and robot training not run</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Research agenda</p><h2><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></h2><p>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.</p><p class="paper-status">Four baseline protocols proposed</p></article></div><p data-search-empty hidden>No publications match this search.</p></section><section class="journal-progress" aria-labelledby="progress"><h2 id="progress">Research footprints</h2><ol class="progress-list"><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Connect visual goals to measured physical feedback</h3><p>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.</p><a href="papers/goal-images-physical-feedback/index.html">Read the architecture and audit</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-native-contact-audit/download-audit.py">Download verified evidence tool</a></div></li><li><time datetime="2026-10-14">14 October 2026</time><div><h3>Separate object difficulty from the gripper and attached tool</h3><p>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.</p><a href="papers/shoe-graspability-v03/index.html">Read the graspability study</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-14-shoe-graspability-v03/lab/index.html">Inspect every recorded attempt</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Measure contact-loss response without claiming a better grasp</h3><p>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.</p><a href="papers/continuous-shoe-grip-v02/index.html">Read the continuous-grip comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-continuous-shoe-grip-v02/lab/index.html">Inspect the recorded attempts</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Make skill prerequisites and failed recovery measurable</h3><p>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.</p><a href="papers/skill-prerequisites-v01/index.html">Read the paired results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-skill-prerequisites-v01/lab/index.html">Explore both recorded experiments</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Move the PiPER arm in a live browser workstation</h3><p>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.</p><a href="papers/live-piper-shoe-workstation-v01/index.html">Read the workstation and evidence</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-live-piper-workstation-v01/workstation/index.html">Open the live workstation</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Load reusable shoes with three native robot arms</h3><p>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.</p><a href="papers/shoe-asset-library-v01/index.html">Read the asset library results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/index.html">Browse shoes and arms</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Complete more placements with a bounded second lift and revised grasp</h3><p>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.</p><a href="papers/piper-reliability-v05/index.html">Read the v0.5 reliability comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-piper-reliability-v05/README.md">Inspect all three cohorts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Separate faster recovery from an ineffective retry</h3><p>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.</p><a href="papers/piper-contact-recovery-v04/index.html">Read the v0.4 recovery comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-recovery-v04/receipt/report.html">Inspect every paired attempt</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Compare probe-only motion with contact history</h3><p>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.</p><a href="papers/piper-contact-history-v03/index.html">Read the contact history comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-contact-v03/receipt/report.html">Inspect all 84 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Test the grip and publish a completion regression</h3><p>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.</p><a href="papers/piper-grip-validation-and-recovery/index.html">Read the paired results and actual stop motion</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-grip/receipt/report.html">Inspect all 30 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Measure PiPER contact and a friction failure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-contact-pilot-retains-a-low-friction-failure">Read the PiPER contact pilot</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Run more arms on a local CPU</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-and-so-101-now-have-runnable-cpu-profiles">Read the profile measurements</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Give the reference arm a contact task</h3><p>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.</p><a href="papers/native-grasp-task-benchmark/index.html">Read the native task benchmark</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-native-grasp/replay/index.html">Replay grasp and placement</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Build a hardware reference we can measure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html">Read the mechanical contract and upcoming arms</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-hardware-reference/replay/index.html">Open the 3D replay</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Learn a compact command-to-motion predictor</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#compact-cpu-pilot-learns-the-next-actuator-boundary">Read the measured pilot and shoe collection plan</a></div></li><li><time datetime="2026-10-10">10 October 2026</time><div><h3>Observe image motion and forecast its path</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#image-motion-source-alpha-estimates-speed-and-future-paths">Read the source alpha and measured limitations</a></div></li><li><time datetime="2026-10-09">9 October 2026</time><div><h3>Publish the robotics research pipeline</h3><p>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.</p><a href="papers/robotics-research-pipeline/index.html">Read the architecture and validation gates</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>A public skill link reaches a simulated arm</h3><p>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.</p><a href="papers/skillspace-arm-installation/index.html">Read the installation contract and arm replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>The offline cable environment is runnable</h3><p>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.</p><a href="papers/offline-cable-training-environment/index.html">Read the first product build and replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Contact accuracy meets runtime</h3><p>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.</p><a href="papers/fast-accurate-robot-training-simulation/index.html">Read the measurements</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Compare the observation-to-skill thesis with prior work</h3><p>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.</p><a href="papers/grok-robotics-research-on-x/index.html">Read the comparison</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Five licensed POV excerpts processed</h3><p>Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.</p><a href="papers/clef-and-grounded-video-observations/index.html">Read the feasibility run</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Define the evidence and release questions</h3><p>Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.</p><a href="papers/physical-skill-research-agenda/index.html">Read the agenda</a></div></li></ol></section><section class="journal-questions"><h2>Open questions</h2><ul><li><a href="topics/capture-quality/index.html">Capture quality</a> — Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</li><li><a href="topics/skill-relations/index.html">Skill relations</a> — Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</li><li><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a> — Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</li><li><a href="topics/physical-grounding/index.html">Physical grounding</a> — Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.</li></ul><p>Follow new publications through <a href="feed.xml">RSS</a> or inspect <a href="https://github.com/Dvidia-Inference/dvidia-research">the source and revisions on GitHub</a>.</p></section><footer class="site-footer"><p>Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p><p><a href="https://github.com/Dvidia-Inference/dvidia-research">Source and revisions</a> · <a href="https://huggingface.co/Dvidia">Hugging Face</a> · <a href="feed.xml">RSS</a> · <a href="https://x.com/iammrriver">@iammrriver</a></p><p>Writing: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE">CC BY 4.0</a>. Original code: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE-CODE">MIT</a>. External work retains its own terms.</p></footer></main></body></html>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="index.html" aria-current="page">Research journal</a><a href="index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="topics/capture-quality/index.html">Capture quality</a><a href="topics/skill-relations/index.html">Skill relations</a><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="index.html" aria-current="page">Research journal</a><a href="index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="topics/capture-quality/index.html">Capture quality</a><a href="topics/skill-relations/index.html">Skill relations</a><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><header class="journal-header"><p class="eyebrow">An open research notebook</p><h1>From observation<br>to physical skill.</h1><p class="journal-intro">We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.</p><p class="paper-meta">Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p></header><section class="journal-publications" aria-labelledby="publications"><div class="section-heading"><h2 id="publications">Publications</h2><p>26 papers and working notes</p></div><div data-paper-search hidden><label for="paper-search">Find a publication</label><input id="paper-search" type="search" autocomplete="off" placeholder="Search titles, questions and findings"><span data-search-count aria-live="polite"></span></div><div class="paper-list"><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-18">18 October 2026</time> · Native diagnostic benchmark and frozen hosted-model protocol</p><h2><a href="papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a></h2><p>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.</p><p class="paper-status">32 native fixtures · 4 identical-image pairs · 64 Jev trials not run</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-18">18 October 2026</time> · Research direction and passive native contact audit</p><h2><a href="papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></h2><p>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.</p><p class="paper-status">14/14 exact passive replays · 249,502 native ticks · visual models unbenchmarked</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-14">14 October 2026</time> · Frozen shape, load and attached-tool diagnostic</p><h2><a href="papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></h2><p>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.</p><p class="paper-status">24/24 valid · 30 g: 8/12 lifts · 300 g: 0/12 · no net gain from removing rake</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Frozen native comparison with independent contact audit</p><h2><a href="papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></h2><p>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.</p><p class="paper-status">20/20 valid · 2/10 completed under both policies · 9 contact-loss alarms verified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Two native environments with authored policies and recorded benchmark evidence</p><h2><a href="papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></h2><p>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.</p><p class="paper-status">16/16 valid · 2 lid contacts avoided · 3/4 retrieval checkpoints · 0/4 retained tool lifts</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-13">13 October 2026</time> · Live browser-native simulation with authored control and separate benchmark evidence</p><h2><a href="papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></h2><p>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.</p><p class="paper-status">Live browser physics · 14/16 targets · strict contact parity failed · shoe lifting unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · Versioned native scene assets and frozen passive-contact benchmark</p><h2><a href="papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></h2><p>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.</p><p class="paper-status">Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · Frozen paired reliability experiment with complete native evidence</p><h2><a href="papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></h2><p>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.</p><p class="paper-status">23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt</p><h2><a href="papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></h2><p>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.</p><p class="paper-status">All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired probe-only and contact-history experiment with complete confirmation receipt</p><h2><a href="papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></h2><p>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.</p><p class="paper-status">History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Paired authored controller experiment and complete confirmation receipt</p><h2><a href="papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></h2><p>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.</p><p class="paper-status">Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Native contact task implementation and authored baseline</p><h2><a href="papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></h2><p>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.</p><p class="paper-status">6/6 simulated placements · controls 0/12 · real-time and hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Pinned mechanics implementation and CPU numerical benchmark</p><h2><a href="papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></h2><p>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.</p><p class="paper-status">Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · Local observation workflow and engineering feasibility pilot</p><h2><a href="papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></h2><p>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.</p><p class="paper-status">Local review release · motion and actuator source pilots · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-09">9 October 2026</time> · Architecture proposal and staged validation roadmap</p><h2><a href="papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></h2><p>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.</p><p class="paper-status">Architecture proposal · current simulation alpha · hardware unqualified</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-08">8 October 2026</time> · Product implementation and measured synthetic pilot</p><h2><a href="papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></h2><p>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&#39;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.</p><p class="paper-status">Public CPU package · one native scene qualified · simulation-only</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Learned movement artifact and installation prototype</p><h2><a href="papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></h2><p>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.</p><p class="paper-status">Simulation-only · teacher/student 7/8 · no overall speedup</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Paired simulation benchmark and product assessment</p><h2><a href="papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></h2><p>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.</p><p class="paper-status">Mixed held-out result · 30/32 baseline, 29/32 updated</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Product prototype and native contact qualification</p><h2><a href="papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></h2><p>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.</p><p class="paper-status">Public skill link to authored adapter · 6/6 scene placements</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Product prototype and local qualification</p><h2><a href="papers/offline-cable-training-environment/index.html">Offline cable training environment</a></h2><p>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.</p><p class="paper-status">CPU simulation-only · Controller search and frozen evaluation executed</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Decision report and local experiment</p><h2><a href="papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></h2><p>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.</p><p class="paper-status">54 CPU fixture trials · GPU and physical transfer untested</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Source review and thesis comparison</p><h2><a href="papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></h2><p>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.</p><p class="paper-status">Primary sources checked · Experiments proposed</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · Research synthesis and proposed experiments</p><h2><a href="papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></h2><p>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.</p><p class="paper-status">Source review · DVIDIA experiments not run</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Engineering feasibility pilot</p><h2><a href="papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></h2><p>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.</p><p class="paper-status">Five licensed excerpts · Accuracy evaluation outstanding</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Source review and implementation proposal</p><h2><a href="papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></h2><p>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.</p><p class="paper-status">Graduation and robot training not run</p></article><article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · Research agenda</p><h2><a href="papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></h2><p>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.</p><p class="paper-status">Four baseline protocols proposed</p></article></div><p data-search-empty hidden>No publications match this search.</p></section><section class="journal-progress" aria-labelledby="progress"><h2 id="progress">Research footprints</h2><ol class="progress-list"><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Make image-goal uncertainty testable</h3><p>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.</p><a href="papers/image-goal-jev-v01/index.html">Read the benchmark and protocol</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html">Compare every current and goal image</a></div></li><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Connect visual goals to measured physical feedback</h3><p>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.</p><a href="papers/goal-images-physical-feedback/index.html">Read the architecture and audit</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-native-contact-audit/download-audit.py">Download verified evidence tool</a></div></li><li><time datetime="2026-10-14">14 October 2026</time><div><h3>Separate object difficulty from the gripper and attached tool</h3><p>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.</p><a href="papers/shoe-graspability-v03/index.html">Read the graspability study</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-14-shoe-graspability-v03/lab/index.html">Inspect every recorded attempt</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Measure contact-loss response without claiming a better grasp</h3><p>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.</p><a href="papers/continuous-shoe-grip-v02/index.html">Read the continuous-grip comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-continuous-shoe-grip-v02/lab/index.html">Inspect the recorded attempts</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Make skill prerequisites and failed recovery measurable</h3><p>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.</p><a href="papers/skill-prerequisites-v01/index.html">Read the paired results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-skill-prerequisites-v01/lab/index.html">Explore both recorded experiments</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Move the PiPER arm in a live browser workstation</h3><p>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.</p><a href="papers/live-piper-shoe-workstation-v01/index.html">Read the workstation and evidence</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-live-piper-workstation-v01/workstation/index.html">Open the live workstation</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Load reusable shoes with three native robot arms</h3><p>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.</p><a href="papers/shoe-asset-library-v01/index.html">Read the asset library results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/index.html">Browse shoes and arms</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Complete more placements with a bounded second lift and revised grasp</h3><p>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.</p><a href="papers/piper-reliability-v05/index.html">Read the v0.5 reliability comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-piper-reliability-v05/README.md">Inspect all three cohorts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Separate faster recovery from an ineffective retry</h3><p>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.</p><a href="papers/piper-contact-recovery-v04/index.html">Read the v0.4 recovery comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-recovery-v04/receipt/report.html">Inspect every paired attempt</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Compare probe-only motion with contact history</h3><p>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.</p><a href="papers/piper-contact-history-v03/index.html">Read the contact history comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-contact-v03/receipt/report.html">Inspect all 84 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Test the grip and publish a completion regression</h3><p>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.</p><a href="papers/piper-grip-validation-and-recovery/index.html">Read the paired results and actual stop motion</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-grip/receipt/report.html">Inspect all 30 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Measure PiPER contact and a friction failure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-contact-pilot-retains-a-low-friction-failure">Read the PiPER contact pilot</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Run more arms on a local CPU</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-and-so-101-now-have-runnable-cpu-profiles">Read the profile measurements</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Give the reference arm a contact task</h3><p>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.</p><a href="papers/native-grasp-task-benchmark/index.html">Read the native task benchmark</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-native-grasp/replay/index.html">Replay grasp and placement</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Build a hardware reference we can measure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html">Read the mechanical contract and upcoming arms</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-hardware-reference/replay/index.html">Open the 3D replay</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Learn a compact command-to-motion predictor</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#compact-cpu-pilot-learns-the-next-actuator-boundary">Read the measured pilot and shoe collection plan</a></div></li><li><time datetime="2026-10-10">10 October 2026</time><div><h3>Observe image motion and forecast its path</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#image-motion-source-alpha-estimates-speed-and-future-paths">Read the source alpha and measured limitations</a></div></li><li><time datetime="2026-10-09">9 October 2026</time><div><h3>Publish the robotics research pipeline</h3><p>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.</p><a href="papers/robotics-research-pipeline/index.html">Read the architecture and validation gates</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>A public skill link reaches a simulated arm</h3><p>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.</p><a href="papers/skillspace-arm-installation/index.html">Read the installation contract and arm replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>The offline cable environment is runnable</h3><p>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.</p><a href="papers/offline-cable-training-environment/index.html">Read the first product build and replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Contact accuracy meets runtime</h3><p>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.</p><a href="papers/fast-accurate-robot-training-simulation/index.html">Read the measurements</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Compare the observation-to-skill thesis with prior work</h3><p>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.</p><a href="papers/grok-robotics-research-on-x/index.html">Read the comparison</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Five licensed POV excerpts processed</h3><p>Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.</p><a href="papers/clef-and-grounded-video-observations/index.html">Read the feasibility run</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Define the evidence and release questions</h3><p>Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.</p><a href="papers/physical-skill-research-agenda/index.html">Read the agenda</a></div></li></ol></section><section class="journal-questions"><h2>Open questions</h2><ul><li><a href="topics/capture-quality/index.html">Capture quality</a> — Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</li><li><a href="topics/skill-relations/index.html">Skill relations</a> — Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</li><li><a href="topics/evaluation-provenance/index.html">Evaluation and provenance</a> — Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</li><li><a href="topics/physical-grounding/index.html">Physical grounding</a> — Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.</li></ul><p>Follow new publications through <a href="feed.xml">RSS</a> or inspect <a href="https://github.com/Dvidia-Inference/dvidia-research">the source and revisions on GitHub</a>.</p></section><footer class="site-footer"><p>Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p><p><a href="https://github.com/Dvidia-Inference/dvidia-research">Source and revisions</a> · <a href="https://huggingface.co/Dvidia">Hugging Face</a> · <a href="feed.xml">RSS</a> · <a href="https://x.com/iammrriver">@iammrriver</a></p><p>Writing: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE">CC BY 4.0</a>. Original code: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE-CODE">MIT</a>. External work retains its own terms.</p></footer></main></body></html>
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-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a 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id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Engineering feasibility pilot</p><h1>Clef and grounded video observations</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Five licensed excerpts · Accuracy evaluation outstanding</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Clef%20and%20grounded%20video%20observations.md">Source & history</a> · <a href="../../downloads/reports/Clef%20and%20grounded%20video%20observations.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#is-clef-a-better-jev">Is Clef a better JEV?</a></li><li><a href="#what-ran">What ran</a></li><li><a href="#product-integration-and-next-research">Product integration and next research</a></li><li><a href="#from-a-command-to-a-learned-physical-skill">From a command to a learned physical skill</a></li></ol></nav></details><div class="paper-body"><p><strong>Engineering pilot executed · 6 October 2026 · Accuracy evaluation outstanding</strong></p>
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href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Engineering feasibility pilot</p><h1>Clef and grounded video observations</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Five licensed excerpts · Accuracy evaluation outstanding</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Clef%20and%20grounded%20video%20observations.md">Source & history</a> · <a href="../../downloads/reports/Clef%20and%20grounded%20video%20observations.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#is-clef-a-better-jev">Is Clef a better JEV?</a></li><li><a href="#what-ran">What ran</a></li><li><a href="#product-integration-and-next-research">Product integration and next research</a></li><li><a href="#from-a-command-to-a-learned-physical-skill">From a command to a learned physical skill</a></li></ol></nav></details><div class="paper-body"><p><strong>Engineering pilot executed · 6 October 2026 · Accuracy evaluation outstanding</strong></p>
 <p>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 <strong>$0.00140634</strong>. This establishes that the image APIs and bounded data contract work; it does not establish that the annotations are accurate.</p>
 <p>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. <strong>Model agreement must not become a “verified” badge.</strong> The observations below come from AI-assisted inspection of the exact input frames after outputs were frozen, not independent human annotations.</p>
 <p><a href="../../downloads/topics/evaluation-provenance/runs/2026-10-06-clef-feasibility/preregistration.md">Preregistration</a> · <a href="../../downloads/topics/evaluation-provenance/runs/2026-10-06-clef-feasibility/results.json">Complete sanitized results and source/frame hashes</a> · <a href="../../downloads/topics/evaluation-provenance/runs/2026-10-06-clef-feasibility/request-recipes.json">Prompts and request recipes</a> · <a href="../../downloads/topics/evaluation-provenance/experiments/0002-skill-graduation.md">Next evaluation protocol</a></p>
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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Local observation workflow and engineering feasibility pilot</p><h1>DVIDIA Observation Lab pilot</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Local review release · motion and actuator source pilots · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/DVIDIA%20Observation%20Lab%20pilot.md">Source & history</a> · <a href="../../downloads/reports/DVIDIA%20Observation%20Lab%20pilot.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-pilot-produces-reviewable-2d-evidence">The pilot produces reviewable 2D evidence</a></li><li><a href="#six-excerpts-establish-integration-not-accuracy">Six excerpts establish integration, not accuracy</a></li><li><a href="#workflow-checks-preserve-failures-and-revisions">Workflow checks preserve failures and revisions</a></li><li><a href="#the-release-keeps-ownership-and-missing-evidence-explicit">The release keeps ownership and missing evidence explicit</a></li><li><a href="#image-motion-source-alpha-estimates-speed-and-future-paths">Image-motion source alpha estimates speed and future paths</a></li><li><a href="#compact-cpu-pilot-learns-the-next-actuator-boundary">Compact CPU pilot learns the next actuator boundary</a></li></ol></nav></details><div class="paper-body"><p><strong>Engineering integration passed on six public excerpts; annotation accuracy remains unmeasured.</strong> 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 <a href="https://github.com/Dvidia-Inference/dvidia-training/tree/d355a4a284cf9bcc4f48dd375ca072d2da188dad">DVIDIA Training commit d355a4a</a>.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="index.html" aria-current="page">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="index.html" aria-current="page">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Local observation workflow and engineering feasibility pilot</p><h1>DVIDIA Observation Lab pilot</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Local review release · motion and actuator source pilots · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/DVIDIA%20Observation%20Lab%20pilot.md">Source & history</a> · <a href="../../downloads/reports/DVIDIA%20Observation%20Lab%20pilot.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-pilot-produces-reviewable-2d-evidence">The pilot produces reviewable 2D evidence</a></li><li><a href="#six-excerpts-establish-integration-not-accuracy">Six excerpts establish integration, not accuracy</a></li><li><a href="#workflow-checks-preserve-failures-and-revisions">Workflow checks preserve failures and revisions</a></li><li><a href="#the-release-keeps-ownership-and-missing-evidence-explicit">The release keeps ownership and missing evidence explicit</a></li><li><a href="#image-motion-source-alpha-estimates-speed-and-future-paths">Image-motion source alpha estimates speed and future paths</a></li><li><a href="#compact-cpu-pilot-learns-the-next-actuator-boundary">Compact CPU pilot learns the next actuator boundary</a></li></ol></nav></details><div class="paper-body"><p><strong>Engineering integration passed on six public excerpts; annotation accuracy remains unmeasured.</strong> 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 <a href="https://github.com/Dvidia-Inference/dvidia-training/tree/d355a4a284cf9bcc4f48dd375ca072d2da188dad">DVIDIA Training commit d355a4a</a>.</p>
 <p><a href="https://github.com/Dvidia-Inference/dvidia-training/blob/observation-lab-v0.1.0/docs/observations.md">Install the Observation Lab release</a> · <a href="https://github.com/Dvidia-Inference/dvidia-training/releases/tag/observation-lab-v0.1.0">GitHub release and downloads</a> · <a href="https://huggingface.co/spaces/Dvidia/observation-lab">Try the synthetic review demo</a> · <a href="../../downloads/topics/evaluation-provenance/runs/2026-10-10-observation-lab/README.md">Frozen engineering receipts</a></p>
 <h2 id="the-pilot-produces-reviewable-2d-evidence">The pilot produces reviewable 2D evidence</h2>
 <p>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. (<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/d355a4a284cf9bcc4f48dd375ca072d2da188dad/docs/observations.md">Observation contract</a>, <a href="../../downloads/topics/evaluation-provenance/runs/2026-10-10-observation-lab/hardened-protocol.json">frozen model and recipe</a>)</p>
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-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a 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quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="index.html" aria-current="page">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Decision report and local experiment</p><h1>Fast accurate robot training simulation</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">54 CPU fixture trials · GPU and physical transfer untested</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Fast%20accurate%20robot%20training%20simulation.md">Source & history</a> · <a href="../../downloads/reports/Fast%20accurate%20robot%20training%20simulation.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#reproduce-the-response-that-changes-the-robot-s-decision">Reproduce the response that changes the robot&#39;s decision</a></li><li><a href="#preserve-the-observation-to-skill-thesis-inside-the-product">Preserve the observation-to-skill thesis inside the product</a></li><li><a href="#select-a-backend-through-task-gates-and-pin-its-dependencies">Select a backend through task gates and pin its dependencies</a></li><li><a href="#make-useful-practice-fast-on-one-owned-gpu-before-stacking-servers">Make useful practice fast on one owned GPU before stacking servers</a></li><li><a href="#qualification-supplies-the-evidence-that-a-download-can-carry">Qualification supplies the evidence that a download can carry</a></li></ol></nav></details><div class="paper-body"><p><strong>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</strong></p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span 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validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="index.html" aria-current="page">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Decision report and local experiment</p><h1>Fast accurate robot training simulation</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">54 CPU fixture trials · GPU and physical transfer untested</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Fast%20accurate%20robot%20training%20simulation.md">Source & history</a> · <a href="../../downloads/reports/Fast%20accurate%20robot%20training%20simulation.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#reproduce-the-response-that-changes-the-robot-s-decision">Reproduce the response that changes the robot&#39;s decision</a></li><li><a href="#preserve-the-observation-to-skill-thesis-inside-the-product">Preserve the observation-to-skill thesis inside the product</a></li><li><a href="#select-a-backend-through-task-gates-and-pin-its-dependencies">Select a backend through task gates and pin its dependencies</a></li><li><a href="#make-useful-practice-fast-on-one-owned-gpu-before-stacking-servers">Make useful practice fast on one owned GPU before stacking servers</a></li><li><a href="#qualification-supplies-the-evidence-that-a-download-can-carry">Qualification supplies the evidence that a download can carry</a></li></ol></nav></details><div class="paper-body"><p><strong>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</strong></p>
 <p><strong>Build an offline, self-hosted environment that converts demonstrations into calibrated practice and qualified robot skills.</strong> 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; <strong>minimum useful VRAM and an “ultrafast” training rate remain unmeasured</strong>. Adding GPUs or servers should scale independent practice worlds, while a released skill retains a declared hardware profile and physical evidence.</p>
 <h2 id="reproduce-the-response-that-changes-the-robot-s-decision">Reproduce the response that changes the robot&#39;s decision</h2>
 <p>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. (<a href="https://www.feynmanlectures.caltech.edu/I_12.html">Feynman on forces and elasticity</a>, <a href="https://www.feynmanlectures.caltech.edu/III_04.html">exclusion and matter</a>)</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>From Skillspace to robot skill · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/from-skillspace-to-robot-skill/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"From Skillspace to robot skill","description":"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.","url":"https://research.dvidia.org/papers/from-skillspace-to-robot-skill/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-06"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe 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href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="index.html" aria-current="page">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Source review and implementation proposal</p><h1>From Skillspace to robot skill</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Graduation and robot training not run</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/From%20Skillspace%20to%20robot%20skill.md">Source & history</a> · <a href="../../downloads/reports/From%20Skillspace%20to%20robot%20skill.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#what-the-linked-work-contributes">What the linked work contributes</a></li><li><a href="#what-dvidia-actually-has">What DVIDIA actually has</a></li><li><a href="#the-proposed-learning-loop">The proposed learning loop</a></li><li><a href="#what-more-videos-should-mean">What “more videos” should mean</a></li><li><a href="#the-bridge-from-phone-video-to-robot-action">The bridge from phone video to robot action</a></li><li><a href="#dofs-and-compatibility">DOFs and compatibility</a></li><li><a href="#graduation-should-require-evidence">Graduation should require evidence</a></li><li><a href="#a-simple-mobile-presentation">A simple mobile presentation</a></li><li><a href="#next-work-in-order">Next work, in order</a></li><li><a href="#sources">Sources</a></li></ol></nav></details><div class="paper-body"><p><strong>Source review and proposal · 6 October 2026 · Graduation experiment not run</strong></p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="index.html" aria-current="page">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="index.html" aria-current="page">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Source review and implementation proposal</p><h1>From Skillspace to robot skill</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Graduation and robot training not run</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/From%20Skillspace%20to%20robot%20skill.md">Source & history</a> · <a href="../../downloads/reports/From%20Skillspace%20to%20robot%20skill.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#what-the-linked-work-contributes">What the linked work contributes</a></li><li><a href="#what-dvidia-actually-has">What DVIDIA actually has</a></li><li><a href="#the-proposed-learning-loop">The proposed learning loop</a></li><li><a href="#what-more-videos-should-mean">What “more videos” should mean</a></li><li><a href="#the-bridge-from-phone-video-to-robot-action">The bridge from phone video to robot action</a></li><li><a href="#dofs-and-compatibility">DOFs and compatibility</a></li><li><a href="#graduation-should-require-evidence">Graduation should require evidence</a></li><li><a href="#a-simple-mobile-presentation">A simple mobile presentation</a></li><li><a href="#next-work-in-order">Next work, in order</a></li><li><a href="#sources">Sources</a></li></ol></nav></details><div class="paper-body"><p><strong>Source review and proposal · 6 October 2026 · Graduation experiment not run</strong></p>
 <p>Follow-up: the <a href="../clef-and-grounded-video-observations/index.html">Clef image feasibility pilot</a> has now run on five public excerpts. It does not complete the 40-episode accuracy protocol below.</p>
 <p>A Skillspace can become the home for a skill&#39;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.</p>
 <p>This review follows <a href="https://x.com/_varunnair/status/2107188112980054270">Varun Nair&#39;s pipeline post</a>, 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.</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Grok robotics research on X · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/grok-robotics-research-on-x/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Grok robotics research on X","description":"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.","url":"https://research.dvidia.org/papers/grok-robotics-research-on-x/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-07"}</script><script src="../../app.js" defer></script></head>
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downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Source review and thesis comparison</p><h1>Grok robotics research on X</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Primary sources checked · Experiments proposed</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Grok%20robotics%20research%20on%20X.md">Source & history</a> · <a href="../../downloads/reports/Grok%20robotics%20research%20on%20X.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-observation-to-skill-thesis-matches-the-evidence-when-each-skill-has-a-tested-operating-region">The observation-to-skill thesis matches the evidence when each skill has a tested operating region</a></li><li><a href="#a-small-video-budget-reuses-knowledge-rather-than-proving-complete-coverage">A small video budget reuses knowledge rather than proving complete coverage</a></li><li><a href="#simulation-becomes-a-skill-through-executable-feedback-and-measured-transfer">Simulation becomes a skill through executable feedback and measured transfer</a></li><li><a href="#four-research-ideas-are-useful-foundations-for-the-virtual-environment-hypothesis">Four research ideas are useful foundations for the virtual-environment hypothesis</a></li><li><a href="#x-supplied-discovery-leads-primary-sources-supplied-the-evidence">X supplied discovery leads; primary sources supplied the evidence</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research snapshot: 7 October 2026 · Grok 4.7 used through Pi CLI · No training, simulation or hardware experiment was run</strong></p>
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reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="index.html" aria-current="page">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Source review and thesis comparison</p><h1>Grok robotics research on X</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Primary sources checked · Experiments proposed</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Grok%20robotics%20research%20on%20X.md">Source & history</a> · <a href="../../downloads/reports/Grok%20robotics%20research%20on%20X.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-observation-to-skill-thesis-matches-the-evidence-when-each-skill-has-a-tested-operating-region">The observation-to-skill thesis matches the evidence when each skill has a tested operating region</a></li><li><a href="#a-small-video-budget-reuses-knowledge-rather-than-proving-complete-coverage">A small video budget reuses knowledge rather than proving complete coverage</a></li><li><a href="#simulation-becomes-a-skill-through-executable-feedback-and-measured-transfer">Simulation becomes a skill through executable feedback and measured transfer</a></li><li><a href="#four-research-ideas-are-useful-foundations-for-the-virtual-environment-hypothesis">Four research ideas are useful foundations for the virtual-environment hypothesis</a></li><li><a href="#x-supplied-discovery-leads-primary-sources-supplied-the-evidence">X supplied discovery leads; primary sources supplied the evidence</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research snapshot: 7 October 2026 · Grok 4.7 used through Pi CLI · No training, simulation or hardware experiment was run</strong></p>
 <p>Your initial thesis has substantial support when expressed as <strong>observation-derived goals and task state, language-based retrieval, contextual spatial attention, and simulation-assisted learning of a feedback skill</strong>. 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.</p>
 <h2 id="the-observation-to-skill-thesis-matches-the-evidence-when-each-skill-has-a-tested-operating-region">The observation-to-skill thesis matches the evidence when each skill has a tested operating region</h2>
 <p>The following mapping preserves the sequence you described. “Supported” means a component has demonstrated precedents, not that the complete proposed architecture has been demonstrated.</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Hardware matched robot simulation · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/hardware-matched-robot-simulation/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Hardware matched robot simulation","description":"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.","url":"https://research.dvidia.org/papers/hardware-matched-robot-simulation/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-11"}</script><script src="../../app.js" defer></script></head>
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aria-current="page">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Pinned mechanics implementation and CPU numerical benchmark</p><h1>Hardware matched robot simulation</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Hardware%20matched%20robot%20simulation.md">Source & history</a> · <a href="../../downloads/reports/Hardware%20matched%20robot%20simulation.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#six-arm-joints-do-not-specify-the-whole-robot">Six arm joints do not specify the whole robot</a></li><li><a href="#inspect-the-arm-and-gripper-in-three-dimensions">Inspect the arm and gripper in three dimensions</a></li><li><a href="#hardware-integration-roadmap">Hardware integration roadmap</a></li><li><a href="#match-observable-mechanics-before-calling-it-fidelity">Match observable mechanics before calling it fidelity</a></li><li><a href="#five-fingers-require-a-new-command-and-feedback-contract">Five fingers require a new command and feedback contract</a></li><li><a href="#offline-training-needs-actions-and-separate-speed-clocks">Offline training needs actions and separate speed clocks</a></li><li><a href="#lerobot-can-collect-evidence-while-dvidia-owns-the-model">LeRobot can collect evidence while DVIDIA owns the model</a></li><li><a href="#eighteen-unloaded-cpu-runs-characterize-this-reference">Eighteen unloaded CPU runs characterize this reference</a></li><li><a href="#piper-and-so-101-now-have-runnable-cpu-profiles">PiPER and SO-101 now have runnable CPU profiles</a></li><li><a href="#piper-contact-pilot-retains-a-low-friction-failure">PiPER contact pilot retains a low-friction failure</a></li><li><a href="#grip-validation-has-a-separately-measured-regression">Grip validation has a separately measured regression</a></li><li><a href="#release-an-evidence-bearing-skill-not-an-appearance-match">Release an evidence-bearing skill, not an appearance match</a></li></ol></nav></details><div class="paper-body"><p><strong>DVIDIA research · 11 October 2026 · Compiled reference assembly and unloaded CPU diagnostics; physical transfer unmeasured</strong></p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="index.html" aria-current="page">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="index.html" aria-current="page">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Pinned mechanics implementation and CPU numerical benchmark</p><h1>Hardware matched robot simulation</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Pinned CPU mechanics · PiPER contact pilot · physical fidelity unmeasured</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Hardware%20matched%20robot%20simulation.md">Source & history</a> · <a href="../../downloads/reports/Hardware%20matched%20robot%20simulation.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#six-arm-joints-do-not-specify-the-whole-robot">Six arm joints do not specify the whole robot</a></li><li><a href="#inspect-the-arm-and-gripper-in-three-dimensions">Inspect the arm and gripper in three dimensions</a></li><li><a href="#hardware-integration-roadmap">Hardware integration roadmap</a></li><li><a href="#match-observable-mechanics-before-calling-it-fidelity">Match observable mechanics before calling it fidelity</a></li><li><a href="#five-fingers-require-a-new-command-and-feedback-contract">Five fingers require a new command and feedback contract</a></li><li><a href="#offline-training-needs-actions-and-separate-speed-clocks">Offline training needs actions and separate speed clocks</a></li><li><a href="#lerobot-can-collect-evidence-while-dvidia-owns-the-model">LeRobot can collect evidence while DVIDIA owns the model</a></li><li><a href="#eighteen-unloaded-cpu-runs-characterize-this-reference">Eighteen unloaded CPU runs characterize this reference</a></li><li><a href="#piper-and-so-101-now-have-runnable-cpu-profiles">PiPER and SO-101 now have runnable CPU profiles</a></li><li><a href="#piper-contact-pilot-retains-a-low-friction-failure">PiPER contact pilot retains a low-friction failure</a></li><li><a href="#grip-validation-has-a-separately-measured-regression">Grip validation has a separately measured regression</a></li><li><a href="#release-an-evidence-bearing-skill-not-an-appearance-match">Release an evidence-bearing skill, not an appearance match</a></li></ol></nav></details><div class="paper-body"><p><strong>DVIDIA research · 11 October 2026 · Compiled reference assembly and unloaded CPU diagnostics; physical transfer unmeasured</strong></p>
 <p><strong>Build the first simulator around a specific six-axis arm and gripper, then measure the physical response that the learner will encounter.</strong> DVIDIA&#39;s vision remains a Skillspace that supplies demonstrations, local training and an installable skill. Rendered images support visual learning, but matching a room&#39;s appearance and the arm&#39;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&#39;s eventual purchase or reproduce that robot&#39;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.</p>
 <h2 id="six-arm-joints-do-not-specify-the-whole-robot">Six arm joints do not specify the whole robot</h2>
 <p>DVIDIA&#39;s existing <code>dvidia-authored-6dof-parallel-jaw-v0</code> 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. (<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/9172c37baeed5925e7bea3af2c92b0bf37a63c24/src/dvidia_training/arm_env.py">Current arm source</a>)</p>
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+<head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Image-goal verification: native benchmark and Jev protocol · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/image-goal-jev-v01/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css?v=editorial-v1"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Image-goal verification: native benchmark and Jev protocol","description":"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.","url":"https://research.dvidia.org/papers/image-goal-jev-v01/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-18"}</script><script src="../../app.js?v=editorial-v1" defer></script></head>
+<body class="workstation-reader"><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section paper-section-nav"><span class="nav-label">In this study</span><a href="#what-is-ready-and-what-remains-unmeasured">What is ready, and what remains unmeasured</a><a href="#the-visual-goal-and-the-physical-goal">The visual goal and the physical goal</a><a href="#eight-conditions-four-layouts">Eight conditions, four layouts</a><a href="#what-we-give-jev">What we give Jev</a><a href="#measure-errors-without-rewarding-an-outage">Measure errors without rewarding an outage</a><a href="#native-measurements-and-current-limits">Native measurements and current limits</a><a href="#run-and-inspect-the-benchmark">Run and inspect the benchmark</a><a href="#what-this-adds-to-skillspace">What this adds to Skillspace</a></div><div class="nav-section"><span class="nav-label">Explore</span><a href="../../index.html">Research journal</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section paper-section-nav"><span class="nav-label">In this study</span><a href="#what-is-ready-and-what-remains-unmeasured">What is ready, and what remains unmeasured</a><a href="#the-visual-goal-and-the-physical-goal">The visual goal and the physical goal</a><a href="#eight-conditions-four-layouts">Eight conditions, four layouts</a><a href="#what-we-give-jev">What we give Jev</a><a href="#measure-errors-without-rewarding-an-outage">Measure errors without rewarding an outage</a><a href="#native-measurements-and-current-limits">Native measurements and current limits</a><a href="#run-and-inspect-the-benchmark">Run and inspect the benchmark</a><a href="#what-this-adds-to-skillspace">What this adds to Skillspace</a></div><div class="nav-section"><span class="nav-label">Explore</span><a href="../../index.html">Research journal</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Native benchmark · Jev evaluation pending</p><h1>Image-goal verification: native benchmark and Jev protocol</h1><p class="paper-meta"><time datetime="2026-10-18">18 October 2026</time> · DVIDIA</p><p class="paper-deck">Does it only look finished?</p><p class="paper-status">32 native fixtures · 4 identical-image pairs · 64 Jev trials not run</p><p class="paper-actions"><a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html">Inspect the image-goal benchmark ↗</a> · <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Image%20goal%20Jev%20v01.md">Source & history</a> · <a href="../../downloads/reports/Image%20goal%20Jev%20v01.md" download>Download Markdown</a></p></header><details class="paper-summary"><summary>Study abstract and scope</summary><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section></details><div class="paper-body"><h2 id="what-is-ready-and-what-remains-unmeasured">What is ready, and what remains unmeasured</h2>
+<p>We have built and validated <strong>32 native PiPER scene fixtures</strong> 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.</p>
+<p><strong>Jev has not been run.</strong> A TypeSafe credential was unavailable for this execution. All 64 planned provider trials remain explicitly <code>not_run</code>, 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.</p>
+<p><a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html">Inspect all current and goal images</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/evaluation-plan.json">Frozen evaluation plan</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/results.json">Explicit provider status</a></p>
+<h2 id="the-visual-goal-and-the-physical-goal">The visual goal and the physical goal</h2>
+<p>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.</p>
+<div class="table-scroll" role="region" aria-label="Research table" tabindex="0"><table><thead><tr><th>Physical requirement</th><th style="text-align:right">Frozen threshold</th></tr></thead><tbody><tr><td>Position error in the table plane</td><td style="text-align:right">At most 25 mm</td></tr><tr><td>Yaw error</td><td style="text-align:right">At most 15°</td></tr><tr><td>Linear speed</td><td style="text-align:right">At most 0.01 m/s</td></tr><tr><td>Angular speed</td><td style="text-align:right">At most 0.05 rad/s</td></tr><tr><td>Table support force</td><td style="text-align:right">Greater than 0.01 N</td></tr><tr><td>Robot contact force</td><td style="text-align:right">At most 0.01 N</td></tr><tr><td>Final qualifying window</td><td style="text-align:right">100 consecutive samples, 2 ms apart</td></tr></tbody></table></div>
+<p>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.</p>
+<p>The PiPER profile uses its existing six arm joints and coupled parallel gripper. The object is a declared <strong>30 g rigid proxy with half the packaged trainer&#39;s width</strong>. Narrowing the object permits contact with the unchanged gripper for this diagnostic. It does not improve the full-size shoe results in our <a href="../shoe-graspability-v03/index.html">previous graspability study</a>, which remain unchanged.</p>
+<p>The model assets are pinned to MuJoCo Menagerie commit <code>0059d4335f8156206f63a35662313385f7ad6d74</code>. Ninety files, totaling 34,562,302 bytes, were verified against the existing lock before generation. The PiPER asset&#39;s MIT notice is retained in the evidence bundle. <a href="https://github.com/google-deepmind/mujoco_menagerie/tree/0059d4335f8156206f63a35662313385f7ad6d74/agilex_piper">Pinned upstream asset</a>.</p>
+<h2 id="eight-conditions-four-layouts">Eight conditions, four layouts</h2>
+<p>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.</p>
+<div class="table-scroll" role="region" aria-label="Research table" tabindex="0"><table><thead><tr><th>Condition</th><th style="text-align:right">Cases</th><th>Physical completion</th><th>Expected judgment from one RGB view</th><th>Expected judgment with native telemetry</th></tr></thead><tbody><tr><td>Settled at the goal</td><td style="text-align:right">4</td><td>Complete</td><td>Unknown</td><td>Complete</td></tr><tr><td>Target missing</td><td style="text-align:right">4</td><td>Incomplete</td><td>Incomplete</td><td>Incomplete</td></tr><tr><td>Orange shoe in place of green target</td><td style="text-align:right">4</td><td>Incomplete</td><td>Incomplete</td><td>Incomplete</td></tr><tr><td>Wrong position</td><td style="text-align:right">4</td><td>Incomplete</td><td>Incomplete</td><td>Incomplete</td></tr><tr><td>Wrong orientation</td><td style="text-align:right">4</td><td>Incomplete</td><td>Incomplete</td><td>Incomplete</td></tr><tr><td>Robot still in contact</td><td style="text-align:right">4</td><td>Incomplete</td><td>Unknown</td><td>Incomplete</td></tr><tr><td>Moving, with the settled pose and pixels</td><td style="text-align:right">4</td><td>Incomplete</td><td>Unknown</td><td>Incomplete</td></tr><tr><td>Completed target under an opaque cover</td><td style="text-align:right">4</td><td>Complete</td><td>Unknown</td><td>Complete</td></tr></tbody></table></div>
+<p>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.</p>
+<p>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 <code>unknown</code> 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.</p>
+<p>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 <code>unknown</code>. For an absent target, wrong identity or clear pose violation, a visible failed requirement is sufficient to answer <code>incomplete</code>.</p>
+<p>The generated cohort contains <strong>8 physically complete and 24 physically incomplete cases</strong>, with <strong>8 absent-target cases</strong> 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.</p>
+<h2 id="what-we-give-jev">What we give Jev</h2>
+<p>The experiment pins TypeSafe&#39;s experimental image-capable <code>jev-1.14.0</code>. 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 <code>complete</code>, <code>incomplete</code> or <code>unknown</code> 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. <a href="https://docs.typesafe.ai/images">Image contract</a>, <a href="https://docs.typesafe.ai/models">model versions</a>.</p>
+<p>The two conditions are:</p>
+<ul>
+<li><strong>RGB:</strong> current and goal images with the task contract.</li>
+<li><strong>RGB plus telemetry:</strong> 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.</li>
+</ul>
+<p>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.</p>
+<p>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.</p>
+<h2 id="measure-errors-without-rewarding-an-outage">Measure errors without rewarding an outage</h2>
+<p>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.</p>
+<div class="table-scroll" role="region" aria-label="Research table" tabindex="0"><table><thead><tr><th>Measure</th><th>Meaning</th></tr></thead><tbody><tr><td>Observability-aware accuracy</td><td>Expected <code>complete</code>, <code>incomplete</code> or <code>unknown</code> verdicts over the planned cohort</td></tr><tr><td>Known-verdict accuracy</td><td>Correct judgments on cases with a defined observable completion verdict</td></tr><tr><td>Justified abstention</td><td><code>unknown</code> on cases whose inputs cannot establish completion</td></tr><tr><td>Physical false completion</td><td>Model says complete while the independent physical reference is incomplete</td></tr><tr><td>Missing-object false presence</td><td>Model says present when the target is physically absent</td></tr><tr><td>Coverage and errors</td><td>Explicit decisions, abstentions, failed requests and unrun cases, reported separately</td></tr><tr><td>Probability error</td><td>Completion-probability Brier score against physical labels, with its valid-response count</td></tr><tr><td>Latency</td><td>p50, p95, p99 and maximum wall time, with errors retained in the all-attempt distribution</td></tr><tr><td>Cost</td><td>Known-usage input-cost estimate and a separate count of attempts with unknown cost</td></tr></tbody></table></div>
+<p>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.</p>
+<p>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.</p>
+<h2 id="native-measurements-and-current-limits">Native measurements and current limits</h2>
+<p>The final local generation took <strong>6.30 wall seconds</strong> 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.</p>
+<p>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.</p>
+<p>An <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/native-validation.json">independent native audit</a> 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 <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/verify-image-goal-fixtures.py">runnable verifier</a> 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.</p>
+<p>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.</p>
+<h2 id="run-and-inspect-the-benchmark">Run and inspect the benchmark</h2>
+<p>The <a href="https://github.com/Dvidia-Inference/dvidia-training/tree/19acf0734d63ecebc07140c8d84302a54baee4c4">native generator and evaluator</a> are frozen at <code>19acf0734d63ecebc07140c8d84302a54baee4c4</code>, based on the published graspability branch. The <a href="https://github.com/Dvidia-Inference/dvidia-training/tree/3d3c8f660f6f9544d6a58c654bab143dd1eddc00">publication source</a> 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.</p>
+<p><a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html">Current/goal viewer</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/cases.json">Every physical label</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json">Native protocol</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json">Complete package checksums</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/README.md">Reproduction and licenses</a></p>
+<p>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.</p>
+<p>Python 3.12+, the repository&#39;s <code>.[arm]</code> 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.</p>
+<p>Jev output is evaluated here, not used for training or distillation. The model remains subject to TypeSafe&#39;s own terms. <a href="https://typesafe.ai/legal/mca">Service agreement</a>.</p>
+<h2 id="what-this-adds-to-skillspace">What this adds to Skillspace</h2>
+<p>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.</p>
+<p>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.</p>
+</div></article><footer class="site-footer"><p>Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p><p><a href="https://github.com/Dvidia-Inference/dvidia-research">Source and revisions</a> · <a href="https://huggingface.co/Dvidia">Hugging Face</a> · <a href="../../feed.xml">RSS</a> · <a href="https://x.com/iammrriver">@iammrriver</a></p><p>Writing: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE">CC BY 4.0</a>. Original code: <a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/LICENSE-CODE">MIT</a>. External work retains its own terms.</p></footer></main></body></html>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Native grasp task benchmark · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/native-grasp-task-benchmark/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Native grasp task benchmark","description":"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.","url":"https://research.dvidia.org/papers/native-grasp-task-benchmark/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-11"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe 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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Native contact task implementation and authored baseline</p><h1>Native grasp task benchmark</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">6/6 simulated placements · controls 0/12 · real-time and hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Native%20grasp%20task%20benchmark.md">Source & history</a> · <a href="../../downloads/reports/Native%20grasp%20task%20benchmark.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#native-contact-makes-the-task-measurable">Native contact makes the task measurable</a></li><li><a href="#frozen-attempts-distinguish-retention-from-a-convincing-replay">Frozen attempts distinguish retention from a convincing replay</a></li><li><a href="#seven-causal-action-outputs-are-the-next-learned-component">Seven causal action outputs are the next learned component</a></li><li><a href="#footage-and-footwear-require-additional-evidence">Footage and footwear require additional evidence</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p>11 October 2026 · Native simulation benchmark and next learning experiment</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="index.html" aria-current="page">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="index.html" aria-current="page">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Native contact task implementation and authored baseline</p><h1>Native grasp task benchmark</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">6/6 simulated placements · controls 0/12 · real-time and hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Native%20grasp%20task%20benchmark.md">Source & history</a> · <a href="../../downloads/reports/Native%20grasp%20task%20benchmark.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#native-contact-makes-the-task-measurable">Native contact makes the task measurable</a></li><li><a href="#frozen-attempts-distinguish-retention-from-a-convincing-replay">Frozen attempts distinguish retention from a convincing replay</a></li><li><a href="#seven-causal-action-outputs-are-the-next-learned-component">Seven causal action outputs are the next learned component</a></li><li><a href="#footage-and-footwear-require-additional-evidence">Footage and footwear require additional evidence</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p>11 October 2026 · Native simulation benchmark and next learning experiment</p>
 <p>DVIDIA&#39;s native contact benchmark completed <strong>6/6 grasp, carry and release placements</strong> across six frozen synthetic layouts; matched open-jaw replay and hold controls completed <strong>0/12</strong>. 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. (<a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-11-native-grasp/replay/receipt/report.json">Frozen confirmation receipt</a>)</p>
 <h2 id="native-contact-makes-the-task-measurable">Native contact makes the task measurable</h2>
 <p>The reference combines <strong>six UR5e joint targets and one Robotiq 2F-85 actuator command</strong>. 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. (<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/ac920cbf70147a7edeb0c706a9b71047f318e12f/src/dvidia_training/reference_task.py">Frozen task source</a>, <a href="https://github.com/Dvidia-Inference/dvidia-training/blob/ac920cbf70147a7edeb0c706a9b71047f318e12f/docs/hardware-reference.md">hardware reference</a>)</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Offline cable training environment · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/offline-cable-training-environment/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Offline cable training environment","description":"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.","url":"https://research.dvidia.org/papers/offline-cable-training-environment/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-07"}</script><script src="../../app.js" defer></script></head>
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id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Product prototype and local qualification</p><h1>Offline cable training environment</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">CPU simulation-only · Controller search and frozen evaluation executed</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Offline%20cable%20training%20environment.md">Source & history</a> · <a href="../../downloads/reports/Offline%20cable%20training%20environment.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-task-has-an-explicit-operating-envelope">The task has an explicit operating envelope</a></li><li><a href="#practice-and-evaluation-ran-with-separate-seeds">Practice and evaluation ran with separate seeds</a></li><li><a href="#numerical-and-offline-checks-have-bounded-meanings">Numerical and offline checks have bounded meanings</a></li><li><a href="#run-and-inspect-the-same-product-foundation">Run and inspect the same product foundation</a></li></ol></nav></details><div class="paper-body"><p><strong>Product prototype and local qualification · 7 October 2026 · Native CPU simulation only</strong></p>
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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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-task-has-an-explicit-operating-envelope">The task has an explicit operating envelope</a></li><li><a href="#practice-and-evaluation-ran-with-separate-seeds">Practice and evaluation ran with separate seeds</a></li><li><a href="#numerical-and-offline-checks-have-bounded-meanings">Numerical and offline checks have bounded meanings</a></li><li><a href="#run-and-inspect-the-same-product-foundation">Run and inspect the same product foundation</a></li></ol></nav></details><div class="paper-body"><p><strong>Product prototype and local qualification · 7 October 2026 · Native CPU simulation only</strong></p>
 <p>The first runnable product loop now connects <strong>an authored environment → local practice → a frozen controller → evaluation → an inspectable skill pack</strong>. <code>CableEndReach-v0</code> 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.</p>
 <p><a href="../../downloads/topics/physical-grounding/runs/2026-10-07-cable-env/README.md">Runnable source and instructions</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-07-cable-env/release-v0/replay.html">Open the recorded replay</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-07-cable-env/release-v0/skill-pack.zip">Download the skill pack</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-07-cable-env/release-v0/qualification.json">Complete qualification</a></p>
 <h2 id="the-task-has-an-explicit-operating-envelope">The task has an explicit operating envelope</h2>
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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Research agenda</p><h1>Turn physical footage into testable evidence</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Four baseline protocols proposed</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Physical%20skill%20research%20agenda.md">Source & history</a> · <a href="../../downloads/reports/Physical%20skill%20research%20agenda.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-essay-motivates-a-hypothesis-not-a-market-forecast">The essay motivates a hypothesis, not a market forecast</a></li><li><a href="#observable-interactions-come-before-richer-labels">Observable interactions come before richer labels</a></li><li><a href="#physical-grounding-requires-narrower-comparisons-and-honest-costs">Physical grounding requires narrower comparisons and honest costs</a></li><li><a href="#provenance-makes-small-results-worth-sharing">Provenance makes small results worth sharing</a></li><li><a href="#contribution-value-comes-from-resolved-uncertainty">Contribution value comes from resolved uncertainty</a></li></ol></nav></details><div class="paper-body"><p>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&#39;s 5 October essay provides a strategic prompt; the technical sources support narrower, testable questions. <strong>The four baseline protocols proposed in this agenda have not run</strong>, and neither customer demand nor robot transfer has been established. Four contributor-sized studies cover capture quality, relations over time, provenance and physical grounding.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="index.html" aria-current="page">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="index.html" aria-current="page">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Research agenda</p><h1>Turn physical footage into testable evidence</h1><p class="paper-meta"><time datetime="2026-10-06">6 October 2026</time> · DVIDIA</p><p class="paper-status">Four baseline protocols proposed</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Physical%20skill%20research%20agenda.md">Source & history</a> · <a href="../../downloads/reports/Physical%20skill%20research%20agenda.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-essay-motivates-a-hypothesis-not-a-market-forecast">The essay motivates a hypothesis, not a market forecast</a></li><li><a href="#observable-interactions-come-before-richer-labels">Observable interactions come before richer labels</a></li><li><a href="#physical-grounding-requires-narrower-comparisons-and-honest-costs">Physical grounding requires narrower comparisons and honest costs</a></li><li><a href="#provenance-makes-small-results-worth-sharing">Provenance makes small results worth sharing</a></li><li><a href="#contribution-value-comes-from-resolved-uncertainty">Contribution value comes from resolved uncertainty</a></li></ol></nav></details><div class="paper-body"><p>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&#39;s 5 October essay provides a strategic prompt; the technical sources support narrower, testable questions. <strong>The four baseline protocols proposed in this agenda have not run</strong>, and neither customer demand nor robot transfer has been established. Four contributor-sized studies cover capture quality, relations over time, provenance and physical grounding.</p>
 <h2 id="the-essay-motivates-a-hypothesis-not-a-market-forecast">The essay motivates a hypothesis, not a market forecast</h2>
 <p>In <em>Robotics Data: Bet Hard Or Get Out</em>, <strong>Sam Padilla, 5 October 2026</strong>, 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. (<a href="https://x.com/theSamPadilla/status/2107237887091843182">Original essay</a>).</p>
 <p>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.</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>PiPER contact history v0.3 · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/piper-contact-history-v03/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"PiPER contact history v0.3","description":"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.","url":"https://research.dvidia.org/papers/piper-contact-history-v03/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-11"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe 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href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="index.html" aria-current="page">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired probe-only and contact-history experiment with complete confirmation receipt</p><h1>PiPER contact history v0.3</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20contact%20history%20v03.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20contact%20history%20v03.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-question-left-by-the-previous-guard">The question left by the previous guard</a></li><li><a href="#a-finite-response-to-changing-contact">A finite response to changing contact</a></li><li><a href="#freeze-the-comparison-before-running-it">Freeze the comparison before running it</a></li><li><a href="#completion-and-recovery-are-separate-outcomes">Completion and recovery are separate outcomes</a></li><li><a href="#actual-motion-and-runtime">Actual motion and runtime</a></li><li><a href="#complete-evidence-and-the-next-test">Complete evidence and the next test</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Contact history v0.3</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="index.html" aria-current="page">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="index.html" aria-current="page">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired probe-only and contact-history experiment with complete confirmation receipt</p><h1>PiPER contact history v0.3</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">History 6/12 · baseline and probe-only 4/12 · controls 0/48 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20contact%20history%20v03.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20contact%20history%20v03.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-question-left-by-the-previous-guard">The question left by the previous guard</a></li><li><a href="#a-finite-response-to-changing-contact">A finite response to changing contact</a></li><li><a href="#freeze-the-comparison-before-running-it">Freeze the comparison before running it</a></li><li><a href="#completion-and-recovery-are-separate-outcomes">Completion and recovery are separate outcomes</a></li><li><a href="#actual-motion-and-runtime">Actual motion and runtime</a></li><li><a href="#complete-evidence-and-the-next-test">Complete evidence and the next test</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Contact history v0.3</p>
 <p><strong>History completes 6/12 placements, versus 4/12 for both original motion and probe-only motion.</strong> 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 <strong>84 planned attempts</strong> remain numerically valid, including <strong>48 unsuccessful negative controls</strong>. 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. (<a href="../../downloads/topics/physical-grounding/runs/2026-10-11-piper-contact-v03/receipt/report.json">Complete original report</a>)</p>
 <h2 id="the-question-left-by-the-previous-guard">The question left by the previous guard</h2>
 <p>The <a href="../piper-grip-validation-and-recovery/index.html">earlier grip experiment</a>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>PiPER contact recovery v0.4 · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/piper-contact-recovery-v04/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"PiPER contact recovery v0.4","description":"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.","url":"https://research.dvidia.org/papers/piper-contact-recovery-v04/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-11"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="index.html" aria-current="page">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a 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href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="index.html" aria-current="page">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt</p><h1>PiPER contact recovery v0.4</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20contact%20recovery%20v04.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20contact%20recovery%20v04.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#diagnose-the-six-failures-first">Diagnose the six failures first</a></li><li><a href="#two-independently-observable-changes">Two independently observable changes</a></li><li><a href="#freeze-development-before-confirmation">Freeze development before confirmation</a></li><li><a href="#completion-recovery-and-refusal">Completion, recovery and refusal</a></li><li><a href="#actual-motion-and-computing-cost">Actual motion and computing cost</a></li><li><a href="#reproduce-and-build-on-the-evidence">Reproduce and build on the evidence</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Contact recovery v0.4</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="index.html" aria-current="page">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="index.html" aria-current="page">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired carry-recovery and supported-acquisition experiment with complete confirmation receipt</p><h1>PiPER contact recovery v0.4</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">All modes 4/12 · two resumed tasks faster · controls 0/48 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20contact%20recovery%20v04.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20contact%20recovery%20v04.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#diagnose-the-six-failures-first">Diagnose the six failures first</a></li><li><a href="#two-independently-observable-changes">Two independently observable changes</a></li><li><a href="#freeze-development-before-confirmation">Freeze development before confirmation</a></li><li><a href="#completion-recovery-and-refusal">Completion, recovery and refusal</a></li><li><a href="#actual-motion-and-computing-cost">Actual motion and computing cost</a></li><li><a href="#reproduce-and-build-on-the-evidence">Reproduce and build on the evidence</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Contact recovery v0.4</p>
 <p><strong>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.</strong> 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 <strong>84 planned attempts</strong> are numerically valid, including <strong>48 failed negative controls</strong>. 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. (<a href="../../downloads/topics/physical-grounding/runs/2026-10-11-piper-recovery-v04/receipt/report.json">Complete frozen report</a>)</p>
 <p>The six failed placements in <a href="../piper-contact-history-v03/index.html">contact history v0.3</a> 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.</p>
 <h2 id="diagnose-the-six-failures-first">Diagnose the six failures first</h2>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>PiPER grip validation and recovery · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/piper-grip-validation-and-recovery/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"PiPER grip validation and recovery","description":"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.","url":"https://research.dvidia.org/papers/piper-grip-validation-and-recovery/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-11"}</script><script src="../../app.js" defer></script></head>
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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired authored controller experiment and complete confirmation receipt</p><h1>PiPER grip validation and recovery</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20grip%20validation%20and%20recovery.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20grip%20validation%20and%20recovery.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#a-pinch-is-not-yet-a-load-bearing-grasp">A pinch is not yet a load-bearing grasp</a></li><li><a href="#check-hold-and-retry-once">Check, hold and retry once</a></li><li><a href="#compare-complete-paired-attempts">Compare complete paired attempts</a></li><li><a href="#separate-qualification-later-following-and-task-success">Separate qualification, later following and task success</a></li><li><a href="#runtime-and-reproducible-evidence">Runtime and reproducible evidence</a></li><li><a href="#what-this-adds-to-skillspace">What this adds to Skillspace</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Authored native simulation experiment</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, 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class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="index.html" aria-current="page">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a 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href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired authored controller experiment and complete confirmation receipt</p><h1>PiPER grip validation and recovery</h1><p class="paper-meta"><time datetime="2026-10-11">11 October 2026</time> · DVIDIA</p><p class="paper-status">Moderate-friction regression · baseline 4/6, guarded 2/6 · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20grip%20validation%20and%20recovery.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20grip%20validation%20and%20recovery.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#a-pinch-is-not-yet-a-load-bearing-grasp">A pinch is not yet a load-bearing grasp</a></li><li><a href="#check-hold-and-retry-once">Check, hold and retry once</a></li><li><a href="#compare-complete-paired-attempts">Compare complete paired attempts</a></li><li><a href="#separate-qualification-later-following-and-task-success">Separate qualification, later following and task success</a></li><li><a href="#runtime-and-reproducible-evidence">Runtime and reproducible evidence</a></li><li><a href="#what-this-adds-to-skillspace">What this adds to Skillspace</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 11 October 2026 · Authored native simulation experiment</p>
 <p><strong>The guarded controller regresses completion at moderate friction: baseline 2/2, guarded 0/2.</strong> 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. (<a href="../../downloads/topics/physical-grounding/runs/2026-10-11-piper-grip/receipt/report.json">Complete original report</a>)</p>
 <h2 id="a-pinch-is-not-yet-a-load-bearing-grasp">A pinch is not yet a load-bearing grasp</h2>
 <p>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. (<a href="../hardware-matched-robot-simulation/index.html#piper-contact-pilot-retains-a-low-friction-failure">Earlier pilot</a>)</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>PiPER placement reliability v0.5 · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/piper-reliability-v05/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"PiPER placement reliability v0.5","description":"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.","url":"https://research.dvidia.org/papers/piper-reliability-v05/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-12"}</script><script src="../../app.js" defer></script></head>
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href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Frozen paired reliability experiment with complete native evidence</p><h1>PiPER placement reliability v0.5</h1><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · DVIDIA</p><p class="paper-status">23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20reliability%20v05.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20reliability%20v05.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#two-changes-one-unchanged-physical-task">Two changes, one unchanged physical task</a></li><li><a href="#separate-development-from-fresh-confirmation">Separate development from fresh confirmation</a></li><li><a href="#complete-placement-outcomes">Complete placement outcomes</a></li><li><a href="#recovery-decisions-and-actual-motion">Recovery decisions and actual motion</a></li><li><a href="#computing-cost-and-offline-execution">Computing cost and offline execution</a></li><li><a href="#reproduce-inspect-and-build-on-the-result">Reproduce, inspect and build on the result</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 12 October 2026 · Placement reliability v0.5</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="index.html" aria-current="page">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a 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class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="index.html" aria-current="page">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Frozen paired reliability experiment with complete native evidence</p><h1>PiPER placement reliability v0.5</h1><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · DVIDIA</p><p class="paper-status">23/36 revised grasp · 15/36 v0.4 · 8 gained, none lost · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/PiPER%20reliability%20v05.md">Source & history</a> · <a href="../../downloads/reports/PiPER%20reliability%20v05.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#two-changes-one-unchanged-physical-task">Two changes, one unchanged physical task</a></li><li><a href="#separate-development-from-fresh-confirmation">Separate development from fresh confirmation</a></li><li><a href="#complete-placement-outcomes">Complete placement outcomes</a></li><li><a href="#recovery-decisions-and-actual-motion">Recovery decisions and actual motion</a></li><li><a href="#computing-cost-and-offline-execution">Computing cost and offline execution</a></li><li><a href="#reproduce-inspect-and-build-on-the-result">Reproduce, inspect and build on the result</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 12 October 2026 · Placement reliability v0.5</p>
 <p><strong>The revised grasp completes 23/36 placements, compared with 15/36 for frozen v0.4 and 18/36 with the second lift alone.</strong> Across three fresh cohorts, it gains eight completions and loses none, reducing failed placements from 21 to 13 in this narrow synthetic comparison. All <strong>252 attempts</strong> are numerically valid; all <strong>144 negative controls fail</strong>, with no solver warnings or prohibited native contact ticks. The fifteen shared successes take <strong>0.772 simulated seconds longer on average</strong> 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. (<a href="../../downloads/topics/physical-grounding/runs/2026-10-12-piper-reliability-v05/analysis.json">Complete aggregate evidence</a>)</p>
 <p>The previous <a href="../piper-contact-recovery-v04/index.html">v0.4 recovery experiment</a> 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.</p>
 <h2 id="two-changes-one-unchanged-physical-task">Two changes, one unchanged physical task</h2>
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-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="index.html" aria-current="page">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a 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href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="index.html" aria-current="page">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Architecture proposal and staged validation roadmap</p><h1>Robotics research pipeline</h1><p class="paper-meta"><time datetime="2026-10-09">9 October 2026</time> · DVIDIA</p><p class="paper-status">Architecture proposal · current simulation alpha · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Robotics%20research%20pipeline.md">Source & history</a> · <a href="../../downloads/reports/Robotics%20research%20pipeline.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#pipeline-at-a-glance">Pipeline at a glance</a></li><li><a href="#1-preserve-originals-propose-labels-separately">1. Preserve originals; propose labels separately</a></li><li><a href="#2-synchronize-measured-state-and-action-intent">2. Synchronize measured state and action intent</a></li><li><a href="#3-train-distinct-models-for-distinct-claims">3. Train distinct models for distinct claims</a></li><li><a href="#4-validate-curation-and-decisions-independently">4. Validate curation and decisions independently</a></li><li><a href="#5-qualify-physics-and-performance-before-expanding-coverage">5. Qualify physics and performance before expanding coverage</a></li><li><a href="#6-release-a-skill-with-its-operating-contract">6. Release a skill with its operating contract</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA&#39;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 <strong>measured robot state</strong>, 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. (<a href="https://github.com/Dvidia-Inference/dvidia-training">Public training package</a>, <a href="https://research.dvidia.org/papers/skillspace-footage-training/">Published measurements</a>)</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="index.html" aria-current="page">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="index.html" aria-current="page">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Architecture proposal and staged validation roadmap</p><h1>Robotics research pipeline</h1><p class="paper-meta"><time datetime="2026-10-09">9 October 2026</time> · DVIDIA</p><p class="paper-status">Architecture proposal · current simulation alpha · hardware unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Robotics%20research%20pipeline.md">Source & history</a> · <a href="../../downloads/reports/Robotics%20research%20pipeline.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#pipeline-at-a-glance">Pipeline at a glance</a></li><li><a href="#1-preserve-originals-propose-labels-separately">1. Preserve originals; propose labels separately</a></li><li><a href="#2-synchronize-measured-state-and-action-intent">2. Synchronize measured state and action intent</a></li><li><a href="#3-train-distinct-models-for-distinct-claims">3. Train distinct models for distinct claims</a></li><li><a href="#4-validate-curation-and-decisions-independently">4. Validate curation and decisions independently</a></li><li><a href="#5-qualify-physics-and-performance-before-expanding-coverage">5. Qualify physics and performance before expanding coverage</a></li><li><a href="#6-release-a-skill-with-its-operating-contract">6. Release a skill with its operating contract</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA&#39;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 <strong>measured robot state</strong>, 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. (<a href="https://github.com/Dvidia-Inference/dvidia-training">Public training package</a>, <a href="https://research.dvidia.org/papers/skillspace-footage-training/">Published measurements</a>)</p>
 <h2 id="pipeline-at-a-glance">Pipeline at a glance</h2>
 <p><figure><img src="https://research.dvidia.org/assets/robotics-pipeline.svg" alt="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." loading="lazy"><figcaption>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.</figcaption></figure></p>
 <p><strong>Figure 1.</strong> 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.</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Reusable shoe asset library v0.1 · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/shoe-asset-library-v01/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Reusable shoe asset library v0.1","description":"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.","url":"https://research.dvidia.org/papers/shoe-asset-library-v01/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-12"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe 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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Versioned native scene assets and frozen passive-contact benchmark</p><h1>Reusable shoe asset library v0.1</h1><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · DVIDIA</p><p class="paper-status">Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Shoe%20asset%20library%20v01.md">Source & history</a> · <a href="../../downloads/reports/Shoe%20asset%20library%20v01.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#what-a-shoe-asset-contains">What a shoe asset contains</a></li><li><a href="#compose-once-reload-explicitly">Compose once, reload explicitly</a></li><li><a href="#benchmark-native-loading-gravity-and-support">Benchmark native loading, gravity and support</a></li><li><a href="#performance-and-practical-requirements">Performance and practical requirements</a></li><li><a href="#how-this-advances-skillspace">How this advances Skillspace</a></li><li><a href="#reproduce-and-inspect">Reproduce and inspect</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 12 October 2026 · Asset library v0.1</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="index.html" aria-current="page">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div 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class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="index.html" aria-current="page">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Versioned native scene assets and frozen passive-contact benchmark</p><h1>Reusable shoe asset library v0.1</h1><p class="paper-meta"><time datetime="2026-10-12">12 October 2026</time> · DVIDIA</p><p class="paper-status">Six shoe proxies · three arms · support 27/27, disabled table 0/27 · manipulation unqualified</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Shoe%20asset%20library%20v01.md">Source & history</a> · <a href="../../downloads/reports/Shoe%20asset%20library%20v01.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#what-a-shoe-asset-contains">What a shoe asset contains</a></li><li><a href="#compose-once-reload-explicitly">Compose once, reload explicitly</a></li><li><a href="#benchmark-native-loading-gravity-and-support">Benchmark native loading, gravity and support</a></li><li><a href="#performance-and-practical-requirements">Performance and practical requirements</a></li><li><a href="#how-this-advances-skillspace">How this advances Skillspace</a></li><li><a href="#reproduce-and-inspect">Reproduce and inspect</a></li></ol></nav></details><div class="paper-body"><p>DVIDIA research · 12 October 2026 · Asset library v0.1</p>
 <p><strong>The first DVIDIA asset library loads six original rigid shoe proxies into native simulation with UR5e/Robotiq, PiPER or SO-101.</strong> 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&#39;s scene descriptor, and reproduce it locally using <code>dvidia-assets</code>. This release supplies reusable scene components and passive contact tests. A shoe-organizing policy is a subsequent task.</p>
 <p><a href="../../downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/index.html">Explore the asset library</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Hugging Face lab</a> · <a href="../../downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/benchmark/report.html">Every benchmark attempt</a></p>
 <h2 id="what-a-shoe-asset-contains">What a shoe asset contains</h2>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Skill capsule acquisition and optimization · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/skill-capsule-acquisition/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Skill capsule acquisition and optimization","description":"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.","url":"https://research.dvidia.org/papers/skill-capsule-acquisition/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-07"}</script><script src="../../app.js" defer></script></head>
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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Learned movement artifact and installation prototype</p><h1>Skill capsule acquisition and optimization</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Simulation-only · teacher/student 7/8 · no overall speedup</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Skill%20capsule%20acquisition%20and%20optimization.md">Source & history</a> · <a href="../../downloads/reports/Skill%20capsule%20acquisition%20and%20optimization.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-acquired-behavior-is-movement-inside-an-authored-task">The acquired behavior is movement inside an authored task</a></li><li><a href="#dragging-a-capsule-should-load-competence-then-test-its-binding">Dragging a capsule should load competence, then test its binding</a></li><li><a href="#native-stepping-dominates-cpu-cost-so-head-compression-is-insufficient">Native stepping dominates CPU cost, so head compression is insufficient</a></li><li><a href="#better-supervision-and-explicit-compatibility-should-guide-expansion">Better supervision and explicit compatibility should guide expansion</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Measured prototype and proposed next steps · 7 October 2026 · Simulation-only</strong></p>
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validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="index.html" aria-current="page">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a 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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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#the-acquired-behavior-is-movement-inside-an-authored-task">The acquired behavior is movement inside an authored task</a></li><li><a href="#dragging-a-capsule-should-load-competence-then-test-its-binding">Dragging a capsule should load competence, then test its binding</a></li><li><a href="#native-stepping-dominates-cpu-cost-so-head-compression-is-insufficient">Native stepping dominates CPU cost, so head compression is insufficient</a></li><li><a href="#better-supervision-and-explicit-compatibility-should-guide-expansion">Better supervision and explicit compatibility should guide expansion</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Measured prototype and proposed next steps · 7 October 2026 · Simulation-only</strong></p>
 <p><strong>DVIDIA now has a compact learned movement artifact that can be packaged for a compatible simulated arm.</strong> A normalized RBF head replaces the teacher&#39;s joint-target computation; task phases, waypoints, gripper/contact supervision, and bounded recovery remain authored. The frozen head contains <strong>34,171 canonical JSON bytes</strong>, and the compiled candidate capsule contains <strong>39,845 bytes</strong>. In a new eight-scene evaluation, teacher and student both completed <strong>7/8</strong>, without teacher fallback, while total episode wall time increased from <strong>6.273 to 6.360 seconds</strong>. 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. (<a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-skill-capsule-v1/benchmark/training.json">Training receipt</a>, <a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-skill-capsule-v1/benchmark/profiling.json">profiling</a>, <a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-skill-capsule-v1/simlab/placement_candidate.skill.json">candidate capsule</a>).</p>
 <h2 id="the-acquired-behavior-is-movement-inside-an-authored-task">The acquired behavior is movement inside an authored task</h2>
 <p>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 <strong>16/16 collection scenes</strong>, generating <strong>6,344 supervised rows</strong>: 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. (<a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-skill-capsule-v1/benchmark/protocol.json">Protocol</a>, <a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-skill-capsule-v1/benchmark/collection.json">collection</a>).</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Skillspace arm benchmarks and evolution · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/skillspace-arm-benchmarks/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Skillspace arm benchmarks and evolution","description":"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.","url":"https://research.dvidia.org/papers/skillspace-arm-benchmarks/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-07"}</script><script src="../../app.js" defer></script></head>
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id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Paired simulation benchmark and product assessment</p><h1>Skillspace arm benchmarks and evolution</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Mixed held-out result · 30/32 baseline, 29/32 updated</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Skillspace%20arm%20benchmarks%20and%20evolution.md">Source & history</a> · <a href="../../downloads/reports/Skillspace%20arm%20benchmarks%20and%20evolution.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#installation-supplies-an-explicit-execution-bridge">Installation supplies an explicit execution bridge</a></li><li><a href="#two-rounds-improved-development-behavior-but-exposed-a-regression">Two rounds improved development behavior but exposed a regression</a></li><li><a href="#contact-dependence-and-numerical-validity-do-not-establish-physical-accuracy">Contact dependence and numerical validity do not establish physical accuracy</a></li><li><a href="#the-cpu-and-offline-results-describe-a-bounded-installed-runtime">The CPU and offline results describe a bounded installed runtime</a></li><li><a href="#media-collections-should-graduate-through-evidence-bearing-releases">Media collections should graduate through evidence-bearing releases</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research and implementation assessment · 7 October 2026 · Simulation-only evidence</strong></p>
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aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#installation-supplies-an-explicit-execution-bridge">Installation supplies an explicit execution bridge</a></li><li><a href="#two-rounds-improved-development-behavior-but-exposed-a-regression">Two rounds improved development behavior but exposed a regression</a></li><li><a href="#contact-dependence-and-numerical-validity-do-not-establish-physical-accuracy">Contact dependence and numerical validity do not establish physical accuracy</a></li><li><a href="#the-cpu-and-offline-results-describe-a-bounded-installed-runtime">The CPU and offline results describe a bounded installed runtime</a></li><li><a href="#media-collections-should-graduate-through-evidence-bearing-releases">Media collections should graduate through evidence-bearing releases</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research and implementation assessment · 7 October 2026 · Simulation-only evidence</strong></p>
 <p><strong>Skillspace → install → simulated arm makes sense as a grounded, compatible execution workflow.</strong> 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 <strong>14/20 to 16/20 to 17/20</strong>, but the frozen held-out comparison declined from <strong>30/32 to 29/32</strong>: 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 <strong>12.283× to 11.388×</strong> 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. (<a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-arm-benchmark-v1/benchmark/assessment.json">Frozen assessment</a>).</p>
 <h2 id="installation-supplies-an-explicit-execution-bridge">Installation supplies an explicit execution bridge</h2>
 <p>The public <code>place_cup</code> 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 <code>contact-pick-place-box-v0</code> adapter for <code>dvidia-authored-6dof-parallel-jaw-v0</code>. 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. (<a href="https://dvidia.org/packs/dvidia/place_cup/skill.json">Public metadata</a>, <a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-07-arm-benchmark-v1/PRODUCT.md">execution guide</a>).</p>
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href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Product prototype and native contact qualification</p><h1>Installing a Skillspace on a simulated arm</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Public skill link to authored adapter · 6/6 scene placements</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Installing%20a%20Skillspace%20on%20a%20simulated%20arm.md">Source & history</a> · <a href="../../downloads/reports/Installing%20a%20Skillspace%20on%20a%20simulated%20arm.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#frozen-scene-evaluation">Frozen scene evaluation</a></li><li><a href="#product-and-evidence">Product and evidence</a></li></ol></nav></details><div class="paper-body"><p>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.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="index.html" aria-current="page">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="index.html" aria-current="page">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Product prototype and native contact qualification</p><h1>Installing a Skillspace on a simulated arm</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Public skill link to authored adapter · 6/6 scene placements</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Installing%20a%20Skillspace%20on%20a%20simulated%20arm.md">Source & history</a> · <a href="../../downloads/reports/Installing%20a%20Skillspace%20on%20a%20simulated%20arm.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#frozen-scene-evaluation">Frozen scene evaluation</a></li><li><a href="#product-and-evidence">Product and evidence</a></li></ol></nav></details><div class="paper-body"><p>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.</p>
 <p>The public example is <a href="https://dvidia.org/store/dvidia/place_cup">DVIDIA place_cup</a>, whose metadata is served at <a href="https://dvidia.org/packs/dvidia/place_cup/skill.json">place_cup/skill.json</a>. The recorded 360-byte source is <code>vdia:0</code>, version <code>0.3.0</code>, with task “Set a cup on the mark.” Its exact-byte SHA-256 is <code>16b4e1ef0f3799345c1d18ce9f1da83b072863ab00ce30a83424080fb467d4ec</code>. 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 <code>physical_robot_ready: false</code>. A later download may differ; its receipt must identify its own bytes.</p>
 <p>In the local studio, one click installs the compatible public envelope and binds <code>contact-pick-place-box-v0</code> to <code>dvidia-authored-6dof-parallel-jaw-v0</code>. 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&#39;s air-gapped deployment remains a separate validation task.</p>
 <p>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.</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Skillspace footage training pipeline · DVIDIA Research</title><meta name="description" content="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&#39;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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/skillspace-footage-training/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Skillspace footage training pipeline","description":"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.","url":"https://research.dvidia.org/papers/skillspace-footage-training/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-08"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="index.html" aria-current="page">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="index.html" aria-current="page">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Product implementation and measured synthetic pilot</p><h1>Skillspace footage training pipeline</h1><p class="paper-meta"><time datetime="2026-10-08">8 October 2026</time> · DVIDIA</p><p class="paper-status">Public CPU package · one native scene qualified · simulation-only</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Skillspace%20footage%20training%20pipeline.md">Source & history</a> · <a href="../../downloads/reports/Skillspace%20footage%20training%20pipeline.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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&#39;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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#published-artifacts-on-hugging-face">Published artifacts on Hugging Face</a></li><li><a href="#actual-media-and-connected-source-groups-gate-training">Actual media and connected source groups gate training</a></li><li><a href="#ten-synthetic-clips-establish-visual-fitting-not-a-shoe-skill">Ten synthetic clips establish visual fitting, not a shoe skill</a></li><li><a href="#executable-movement-requires-aligned-actions-and-an-adapter">Executable movement requires aligned actions and an adapter</a></li><li><a href="#8-october-2026-frozen-sample-count-follow-up">8 October 2026: frozen sample-count follow-up</a></li><li><a href="#the-next-experiment-should-measure-the-missing-bridge">The next experiment should measure the missing bridge</a></li></ol></nav></details><div class="paper-body"><p><strong>Research pipeline evolution — 9 October 2026.</strong> The <a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a> 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.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="index.html" aria-current="page">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="index.html" aria-current="page">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Product implementation and measured synthetic pilot</p><h1>Skillspace footage training pipeline</h1><p class="paper-meta"><time datetime="2026-10-08">8 October 2026</time> · DVIDIA</p><p class="paper-status">Public CPU package · one native scene qualified · simulation-only</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Skillspace%20footage%20training%20pipeline.md">Source & history</a> · <a href="../../downloads/reports/Skillspace%20footage%20training%20pipeline.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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&#39;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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#published-artifacts-on-hugging-face">Published artifacts on Hugging Face</a></li><li><a href="#actual-media-and-connected-source-groups-gate-training">Actual media and connected source groups gate training</a></li><li><a href="#ten-synthetic-clips-establish-visual-fitting-not-a-shoe-skill">Ten synthetic clips establish visual fitting, not a shoe skill</a></li><li><a href="#executable-movement-requires-aligned-actions-and-an-adapter">Executable movement requires aligned actions and an adapter</a></li><li><a href="#8-october-2026-frozen-sample-count-follow-up">8 October 2026: frozen sample-count follow-up</a></li><li><a href="#the-next-experiment-should-measure-the-missing-bridge">The next experiment should measure the missing bridge</a></li></ol></nav></details><div class="paper-body"><p><strong>Research pipeline evolution — 9 October 2026.</strong> The <a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a> 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.</p>
 <p>The public, standalone <a href="https://github.com/Dvidia-Inference/dvidia-training">DVIDIA training repository</a> 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. <strong>Video prediction and robot movement remain separate claims.</strong> Ten synthetic six-second clips demonstrate visual fitting; simulator telemetry supplies the movement pilot&#39;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. (<a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-08-footage-pipeline-v1/release/dvidia-training-v0.1.zip">Standalone source archive</a>, <a href="https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-08-footage-pipeline-v1/qualification/qualification.json">Qualification receipt</a>)</p>
 <h2 id="published-artifacts-on-hugging-face">Published artifacts on Hugging Face</h2>
 <p>The <a href="https://huggingface.co/Dvidia">DVIDIA organization on Hugging Face</a> mirrors the measured pilot for inspection and local reproduction:</p>
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Spatially grounded downloadable robot skills · DVIDIA Research</title><meta name="description" content="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."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Spatially grounded downloadable robot skills","description":"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.","url":"https://research.dvidia.org/papers/spatially-grounded-downloadable-robot-skills/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"},"datePublished":"2026-10-07"}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="index.html" aria-current="page">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="index.html" aria-current="page">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Research synthesis and proposed experiments</p><h1>Spatially grounded downloadable robot skills</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Source review · DVIDIA experiments not run</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Spatially%20grounded%20downloadable%20robot%20skills.md">Source & history</a> · <a href="../../downloads/reports/Spatially%20grounded%20downloadable%20robot%20skills.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#demonstrations-teach-meaning-before-they-supply-motor-control">Demonstrations teach meaning before they supply motor control</a></li><li><a href="#spatial-priors-can-shorten-inspection-without-determining-the-task">Spatial priors can shorten inspection without determining the task</a></li><li><a href="#simulation-expands-grounded-experience-physical-tests-establish-competence">Simulation expands grounded experience; physical tests establish competence</a></li><li><a href="#download-the-contract-compatible-controller-and-evidence-together">Download the contract, compatible controller, and evidence together</a></li><li><a href="#dvidia-should-test-data-value-before-scaling-the-library">DVIDIA should test data value before scaling the library</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research synthesis and proposed experiments · 7 October 2026 · No DVIDIA experiment in this report has run</strong></p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="index.html" aria-current="page">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="index.html" aria-current="page">Spatially grounded downloadable robot skills</a><a href="../clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../../topics/capture-quality/index.html">Capture quality</a><a href="../../topics/skill-relations/index.html">Skill relations</a><a href="../../topics/evaluation-provenance/index.html">Evaluation and provenance</a><a href="../../topics/physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper"><header class="paper-header"><p class="eyebrow">Research synthesis and proposed experiments</p><h1>Spatially grounded downloadable robot skills</h1><p class="paper-meta"><time datetime="2026-10-07">7 October 2026</time> · DVIDIA</p><p class="paper-status">Source review · DVIDIA experiments not run</p><p class="paper-actions"><a href="https://github.com/Dvidia-Inference/dvidia-research/blob/main/reports/Spatially%20grounded%20downloadable%20robot%20skills.md">Source & history</a> · <a href="../../downloads/reports/Spatially%20grounded%20downloadable%20robot%20skills.md" download>Download Markdown</a></p></header><section class="paper-abstract" aria-label="Abstract"><h2>Abstract</h2><p>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.</p></section><details class="paper-toc"><summary>In this paper</summary><nav aria-label="Table of contents"><ol><li><a href="#demonstrations-teach-meaning-before-they-supply-motor-control">Demonstrations teach meaning before they supply motor control</a></li><li><a href="#spatial-priors-can-shorten-inspection-without-determining-the-task">Spatial priors can shorten inspection without determining the task</a></li><li><a href="#simulation-expands-grounded-experience-physical-tests-establish-competence">Simulation expands grounded experience; physical tests establish competence</a></li><li><a href="#download-the-contract-compatible-controller-and-evidence-together">Download the contract, compatible controller, and evidence together</a></li><li><a href="#dvidia-should-test-data-value-before-scaling-the-library">DVIDIA should test data value before scaling the library</a></li><li><a href="#conclusion">Conclusion</a></li></ol></nav></details><div class="paper-body"><p><strong>Research synthesis and proposed experiments · 7 October 2026 · No DVIDIA experiment in this report has run</strong></p>
 <p>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. <strong>Fifty examples could adapt a substantial pretrained physical skill model; they are not an established threshold for acquiring all of shoe tying.</strong> The falsifiable research claim is: <em>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</em>. A downloadable skill is the resulting tested controller, its compatibility and task contract, and evidence of where it works.</p>
 <p>This synthesis combines three parallel primary-source research tracks with the existing DVIDIA research context. <strong>Grok 4.7 was inaccessible and was not used</strong>: 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.</p>
 <h2 id="demonstrations-teach-meaning-before-they-supply-motor-control">Demonstrations teach meaning before they supply motor control</h2>
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",
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Capture quality · DVIDIA Research</title><meta name="description" content="Test whether hands, relevant objects and state changes are actually observable in a POV demonstration."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/topics/capture-quality/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Capture quality","description":"Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.","url":"https://research.dvidia.org/topics/capture-quality/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"}}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="index.html" aria-current="page">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="index.html" aria-current="page">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Capture quality</h1><p class="paper-meta">DVIDIA · Visible evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="index.html" aria-current="page">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="index.html" aria-current="page">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Capture quality</h1><p class="paper-meta">DVIDIA · Visible evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Test whether hands, relevant objects and state changes are actually observable in a POV demonstration.</p>
 <p>Status: <strong>scoping</strong> · Updated 6 October 2026 · Results: <strong>not run</strong></p>
 <p><a href="https://github.com/Dvidia-Inference/dvidia-research/issues/new?template=research-question.yml">Open a research question</a> · <a href="../../downloads/topics/capture-quality/experiments/0001-baseline.md">Baseline protocol</a> · <a href="../../downloads/topics/capture-quality/references.bib">Bibliography</a></p>
 <h2 id="primary-sources">Primary sources</h2>
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-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a 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href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="index.html" aria-current="page">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div 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recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="index.html" aria-current="page">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Evaluation and provenance</h1><p class="paper-meta">DVIDIA · Reproducible evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a></li><li><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="index.html" aria-current="page">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="index.html" aria-current="page">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Evaluation and provenance</h1><p class="paper-meta">DVIDIA · Reproducible evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a></li><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a></li><li><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Test episode readback, original-to-derivative lineage and split integrity using small, auditable fixtures.</p>
 <p>Status: <strong>evaluating</strong> · Updated 6 October 2026 · Results: <strong>image feasibility run complete; reference accuracy and provenance baselines not run</strong></p>
 <p><a href="https://github.com/Dvidia-Inference/dvidia-research/issues/new?template=research-question.yml">Open a research question</a> · <a href="../../downloads/topics/evaluation-provenance/experiments/0001-baseline.md">Baseline protocol</a> · <a href="../../downloads/topics/evaluation-provenance/references.bib">Bibliography</a></p>
 <h2 id="primary-sources">Primary sources</h2>
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-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a 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recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a 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task.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a></li><li><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></li><li><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><h2 id="live-piper-shoe-workstation-13-october-2026">Live PiPER shoe workstation — 13 October 2026</h2>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="index.html" aria-current="page">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="../skill-relations/index.html">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="index.html" aria-current="page">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Physical grounding</h1><p class="paper-meta">DVIDIA · Signals beyond appearance</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Measure whether state/contact evidence and controlled synthetic augmentation improve a defined physical-skill task.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a></li><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a></li><li><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a></li><li><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><h2 id="live-piper-shoe-workstation-13-october-2026">Live PiPER shoe workstation — 13 October 2026</h2>
 <p><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a>
 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
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 <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Skill relations · DVIDIA Research</title><meta name="description" content="Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals."><meta name="theme-color" content="#faf9f6"><link rel="canonical" href="https://research.dvidia.org/topics/skill-relations/"><link rel="alternate" type="application/rss+xml" title="DVIDIA Research" href="../../feed.xml"><link rel="icon" href="../../assets/favicon.svg" type="image/svg+xml"><link rel="stylesheet" href="../../styles.css"><script type="application/ld+json">{"@context":"https://schema.org","@type":"Article","headline":"Skill relations","description":"Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.","url":"https://research.dvidia.org/topics/skill-relations/","publisher":{"@type":"Organization","name":"DVIDIA","url":"https://dvidia.org"}}</script><script src="../../app.js" defer></script></head>
-<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="index.html" aria-current="page">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div 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recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="index.html" aria-current="page">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Skill relations</h1><p class="paper-meta">DVIDIA · Events with evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</p>
+<body><a class="skip-link" href="#main">Skip to content</a><aside class="site-sidebar" aria-label="Research navigation"><a class="brand" href="../../index.html"><span class="brand-name">DVIDIA</span><span class="brand-subtitle">Research notebook</span></a><nav class="site-nav" id="site-navigation" aria-label="Main"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="index.html" aria-current="page">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav><details class="mobile-navigation"><summary>Contents</summary><nav aria-label="Mobile"><div class="nav-section"><span class="nav-label">Notebook</span><a href="../../index.html">Research journal</a><a href="../../index.html#progress">Progress</a></div><div class="nav-section"><span class="nav-label">Publications</span><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a><a href="../../papers/dvidia-observation-lab/index.html">DVIDIA Observation Lab pilot</a><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a><a href="../../papers/skillspace-footage-training/index.html">Skillspace footage training pipeline</a><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a><a href="../../papers/offline-cable-training-environment/index.html">Offline cable training environment</a><a href="../../papers/fast-accurate-robot-training-simulation/index.html">Fast accurate robot training simulation</a><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></div><div class="nav-section"><span class="nav-label">Questions</span><a href="../capture-quality/index.html">Capture quality</a><a href="index.html" aria-current="page">Skill relations</a><a href="../evaluation-provenance/index.html">Evaluation and provenance</a><a href="../physical-grounding/index.html">Physical grounding</a></div><div class="nav-section"><span class="nav-label">Follow the work</span><a href="../../feed.xml">RSS feed</a><a href="https://github.com/Dvidia-Inference/dvidia-research">GitHub</a><a href="https://huggingface.co/Dvidia">Hugging Face</a><a href="https://x.com/iammrriver">X · @iammrriver</a><a href="mailto:hello@dvidia.org">hello@dvidia.org</a></div></nav></details></aside><main class="page-shell" id="main"><article class="paper topic-paper"><header class="paper-header"><p class="eyebrow">Research question</p><h1>Skill relations</h1><p class="paper-meta">DVIDIA · Events with evidence</p><p class="paper-status">Protocols and progress are scoped below</p></header><section class="paper-abstract"><h2>Question</h2><p>Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</p></section><section class="related-papers"><h2>Publications in this area</h2><ul><li><a href="../../papers/image-goal-jev-v01/index.html">Image-goal verification: native benchmark and Jev protocol</a></li><li><a href="../../papers/goal-images-physical-feedback/index.html">A visual goal still needs physical feedback</a></li><li><a href="../../papers/shoe-graspability-v03/index.html">Give the gripper a fair test</a></li><li><a href="../../papers/continuous-shoe-grip-v02/index.html">From reaching a shoe to keeping hold</a></li><li><a href="../../papers/skill-prerequisites-v01/index.html">Skill prerequisites: access, reach and recovery</a></li><li><a href="../../papers/live-piper-shoe-workstation-v01/index.html">Live PiPER shoe workstation v0.1</a></li><li><a href="../../papers/shoe-asset-library-v01/index.html">Reusable shoe asset library v0.1</a></li><li><a href="../../papers/piper-reliability-v05/index.html">PiPER placement reliability v0.5</a></li><li><a href="../../papers/piper-contact-recovery-v04/index.html">PiPER contact recovery v0.4</a></li><li><a href="../../papers/piper-contact-history-v03/index.html">PiPER contact history v0.3</a></li><li><a href="../../papers/piper-grip-validation-and-recovery/index.html">PiPER grip validation and recovery</a></li><li><a href="../../papers/native-grasp-task-benchmark/index.html">Native grasp task benchmark</a></li><li><a href="../../papers/hardware-matched-robot-simulation/index.html">Hardware matched robot simulation</a></li><li><a href="../../papers/robotics-research-pipeline/index.html">Robotics research pipeline</a></li><li><a href="../../papers/skill-capsule-acquisition/index.html">Skill capsule acquisition and optimization</a></li><li><a href="../../papers/skillspace-arm-benchmarks/index.html">Skillspace arm benchmarks and evolution</a></li><li><a href="../../papers/skillspace-arm-installation/index.html">Installing a Skillspace on a simulated arm</a></li><li><a href="../../papers/grok-robotics-research-on-x/index.html">Grok robotics research on X</a></li><li><a href="../../papers/spatially-grounded-downloadable-robot-skills/index.html">Spatially grounded downloadable robot skills</a></li><li><a href="../../papers/clef-and-grounded-video-observations/index.html">Clef and grounded video observations</a></li><li><a href="../../papers/from-skillspace-to-robot-skill/index.html">From Skillspace to robot skill</a></li><li><a href="../../papers/physical-skill-research-agenda/index.html">Turn physical footage into testable evidence</a></li></ul></section><div class="paper-body"><p><strong>Question:</strong> Calibrate hand–object relations, then test temporal event graphs whose claims point back to video intervals.</p>
 <p>Status: <strong>scoping</strong> · Updated 6 October 2026 · Results: <strong>not run</strong></p>
 <p><a href="https://github.com/Dvidia-Inference/dvidia-research/issues/new?template=research-question.yml">Open a research question</a> · <a href="../../downloads/topics/skill-relations/experiments/0001-baseline.md">Baseline protocol</a> · <a href="../../downloads/topics/skill-relations/references.bib">Bibliography</a></p>
 <h2 id="primary-sources">Primary sources</h2>
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 => `<article class="paper-row" data-paper-row><p class="paper-meta"><time datetime="${p.date}">${dateLabel(p.date)}</time> · ${escapeHtml(p.type)}</p><h2><a href="${href("index.html", pageMap.get(p.source))}">${escapeHtml(p.title)}</a></h2><p>${escapeHtml(p.abstract)}</p><p class="paper-status">${escapeHtml(p.status)}</p></article>`).join("");
-  const content = `<header class="journal-header"><p class="eyebrow">An open research notebook</p><h1>From observation<br>to physical skill.</h1><p class="journal-intro">We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.</p><p class="paper-meta">Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p></header><section class="journal-publications" aria-labelledby="publications"><div class="section-heading"><h2 id="publications">Publications</h2><p>${catalog.publications.length} papers and working notes</p></div><div data-paper-search hidden><label for="paper-search">Find a publication</label><input id="paper-search" type="search" autocomplete="off" placeholder="Search titles, questions and findings"><span data-search-count aria-live="polite"></span></div><div class="paper-list">${paperRows}</div><p data-search-empty hidden>No publications match this search.</p></section><section class="journal-progress" aria-labelledby="progress"><h2 id="progress">Research footprints</h2><ol class="progress-list"><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Connect visual goals to measured physical feedback</h3><p>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.</p><a href="papers/goal-images-physical-feedback/index.html">Read the architecture and audit</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-native-contact-audit/download-audit.py">Download verified evidence tool</a></div></li><li><time datetime="2026-10-14">14 October 2026</time><div><h3>Separate object difficulty from the gripper and attached tool</h3><p>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.</p><a href="papers/shoe-graspability-v03/index.html">Read the graspability study</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-14-shoe-graspability-v03/lab/index.html">Inspect every recorded attempt</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Measure contact-loss response without claiming a better grasp</h3><p>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.</p><a href="papers/continuous-shoe-grip-v02/index.html">Read the continuous-grip comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-continuous-shoe-grip-v02/lab/index.html">Inspect the recorded attempts</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Make skill prerequisites and failed recovery measurable</h3><p>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.</p><a href="papers/skill-prerequisites-v01/index.html">Read the paired results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-skill-prerequisites-v01/lab/index.html">Explore both recorded experiments</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Move the PiPER arm in a live browser workstation</h3><p>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.</p><a href="papers/live-piper-shoe-workstation-v01/index.html">Read the workstation and evidence</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-live-piper-workstation-v01/workstation/index.html">Open the live workstation</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Load reusable shoes with three native robot arms</h3><p>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.</p><a href="papers/shoe-asset-library-v01/index.html">Read the asset library results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/index.html">Browse shoes and arms</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Complete more placements with a bounded second lift and revised grasp</h3><p>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.</p><a href="papers/piper-reliability-v05/index.html">Read the v0.5 reliability comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-piper-reliability-v05/README.md">Inspect all three cohorts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Separate faster recovery from an ineffective retry</h3><p>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.</p><a href="papers/piper-contact-recovery-v04/index.html">Read the v0.4 recovery comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-recovery-v04/receipt/report.html">Inspect every paired attempt</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Compare probe-only motion with contact history</h3><p>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.</p><a href="papers/piper-contact-history-v03/index.html">Read the contact history comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-contact-v03/receipt/report.html">Inspect all 84 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Test the grip and publish a completion regression</h3><p>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.</p><a href="papers/piper-grip-validation-and-recovery/index.html">Read the paired results and actual stop motion</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-grip/receipt/report.html">Inspect all 30 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Measure PiPER contact and a friction failure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-contact-pilot-retains-a-low-friction-failure">Read the PiPER contact pilot</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Run more arms on a local CPU</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-and-so-101-now-have-runnable-cpu-profiles">Read the profile measurements</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Give the reference arm a contact task</h3><p>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.</p><a href="papers/native-grasp-task-benchmark/index.html">Read the native task benchmark</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-native-grasp/replay/index.html">Replay grasp and placement</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Build a hardware reference we can measure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html">Read the mechanical contract and upcoming arms</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-hardware-reference/replay/index.html">Open the 3D replay</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Learn a compact command-to-motion predictor</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#compact-cpu-pilot-learns-the-next-actuator-boundary">Read the measured pilot and shoe collection plan</a></div></li><li><time datetime="2026-10-10">10 October 2026</time><div><h3>Observe image motion and forecast its path</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#image-motion-source-alpha-estimates-speed-and-future-paths">Read the source alpha and measured limitations</a></div></li><li><time datetime="2026-10-09">9 October 2026</time><div><h3>Publish the robotics research pipeline</h3><p>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.</p><a href="papers/robotics-research-pipeline/index.html">Read the architecture and validation gates</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>A public skill link reaches a simulated arm</h3><p>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.</p><a href="papers/skillspace-arm-installation/index.html">Read the installation contract and arm replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>The offline cable environment is runnable</h3><p>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.</p><a href="papers/offline-cable-training-environment/index.html">Read the first product build and replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Contact accuracy meets runtime</h3><p>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.</p><a href="papers/fast-accurate-robot-training-simulation/index.html">Read the measurements</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Compare the observation-to-skill thesis with prior work</h3><p>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.</p><a href="papers/grok-robotics-research-on-x/index.html">Read the comparison</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Five licensed POV excerpts processed</h3><p>Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.</p><a href="papers/clef-and-grounded-video-observations/index.html">Read the feasibility run</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Define the evidence and release questions</h3><p>Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.</p><a href="papers/physical-skill-research-agenda/index.html">Read the agenda</a></div></li></ol></section><section class="journal-questions"><h2>Open questions</h2><ul>${topics.map(t => `<li><a href="topics/${t.id}/index.html">${escapeHtml(t.title)}</a> — ${escapeHtml(t.summary)}</li>`).join("")}</ul><p>Follow new publications through <a href="feed.xml">RSS</a> or inspect <a href="${REPOSITORY}">the source and revisions on GitHub</a>.</p></section>`;
+  const content = `<header class="journal-header"><p class="eyebrow">An open research notebook</p><h1>From observation<br>to physical skill.</h1><p class="journal-intro">We study how demonstrations become usable evidence, how simulation becomes useful practice, and when a robot skill earns a release.</p><p class="paper-meta">Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a></p></header><section class="journal-publications" aria-labelledby="publications"><div class="section-heading"><h2 id="publications">Publications</h2><p>${catalog.publications.length} papers and working notes</p></div><div data-paper-search hidden><label for="paper-search">Find a publication</label><input id="paper-search" type="search" autocomplete="off" placeholder="Search titles, questions and findings"><span data-search-count aria-live="polite"></span></div><div class="paper-list">${paperRows}</div><p data-search-empty hidden>No publications match this search.</p></section><section class="journal-progress" aria-labelledby="progress"><h2 id="progress">Research footprints</h2><ol class="progress-list"><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Make image-goal uncertainty testable</h3><p>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.</p><a href="papers/image-goal-jev-v01/index.html">Read the benchmark and protocol</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/index.html">Compare every current and goal image</a></div></li><li><time datetime="2026-10-18">18 October 2026</time><div><h3>Connect visual goals to measured physical feedback</h3><p>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.</p><a href="papers/goal-images-physical-feedback/index.html">Read the architecture and audit</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-18-native-contact-audit/download-audit.py">Download verified evidence tool</a></div></li><li><time datetime="2026-10-14">14 October 2026</time><div><h3>Separate object difficulty from the gripper and attached tool</h3><p>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.</p><a href="papers/shoe-graspability-v03/index.html">Read the graspability study</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-14-shoe-graspability-v03/lab/index.html">Inspect every recorded attempt</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Measure contact-loss response without claiming a better grasp</h3><p>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.</p><a href="papers/continuous-shoe-grip-v02/index.html">Read the continuous-grip comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-continuous-shoe-grip-v02/lab/index.html">Inspect the recorded attempts</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Make skill prerequisites and failed recovery measurable</h3><p>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.</p><a href="papers/skill-prerequisites-v01/index.html">Read the paired results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-skill-prerequisites-v01/lab/index.html">Explore both recorded experiments</a></div></li><li><time datetime="2026-10-13">13 October 2026</time><div><h3>Move the PiPER arm in a live browser workstation</h3><p>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.</p><a href="papers/live-piper-shoe-workstation-v01/index.html">Read the workstation and evidence</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-13-live-piper-workstation-v01/workstation/index.html">Open the live workstation</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Load reusable shoes with three native robot arms</h3><p>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.</p><a href="papers/shoe-asset-library-v01/index.html">Read the asset library results</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-shoe-asset-library-v01/library/index.html">Browse shoes and arms</a></div></li><li><time datetime="2026-10-12">12 October 2026</time><div><h3>Complete more placements with a bounded second lift and revised grasp</h3><p>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.</p><a href="papers/piper-reliability-v05/index.html">Read the v0.5 reliability comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-12-piper-reliability-v05/README.md">Inspect all three cohorts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Separate faster recovery from an ineffective retry</h3><p>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.</p><a href="papers/piper-contact-recovery-v04/index.html">Read the v0.4 recovery comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-recovery-v04/receipt/report.html">Inspect every paired attempt</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Compare probe-only motion with contact history</h3><p>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.</p><a href="papers/piper-contact-history-v03/index.html">Read the contact history comparison</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-contact-v03/receipt/report.html">Inspect all 84 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Test the grip and publish a completion regression</h3><p>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.</p><a href="papers/piper-grip-validation-and-recovery/index.html">Read the paired results and actual stop motion</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-piper-grip/receipt/report.html">Inspect all 30 attempts</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Measure PiPER contact and a friction failure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-contact-pilot-retains-a-low-friction-failure">Read the PiPER contact pilot</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Run more arms on a local CPU</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html#piper-and-so-101-now-have-runnable-cpu-profiles">Read the profile measurements</a> · <a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Open the Robot Simulation Lab</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Give the reference arm a contact task</h3><p>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.</p><a href="papers/native-grasp-task-benchmark/index.html">Read the native task benchmark</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-native-grasp/replay/index.html">Replay grasp and placement</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Build a hardware reference we can measure</h3><p>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.</p><a href="papers/hardware-matched-robot-simulation/index.html">Read the mechanical contract and upcoming arms</a> · <a href="downloads/topics/physical-grounding/runs/2026-10-11-hardware-reference/replay/index.html">Open the 3D replay</a></div></li><li><time datetime="2026-10-11">11 October 2026</time><div><h3>Learn a compact command-to-motion predictor</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#compact-cpu-pilot-learns-the-next-actuator-boundary">Read the measured pilot and shoe collection plan</a></div></li><li><time datetime="2026-10-10">10 October 2026</time><div><h3>Observe image motion and forecast its path</h3><p>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.</p><a href="papers/dvidia-observation-lab/index.html#image-motion-source-alpha-estimates-speed-and-future-paths">Read the source alpha and measured limitations</a></div></li><li><time datetime="2026-10-09">9 October 2026</time><div><h3>Publish the robotics research pipeline</h3><p>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.</p><a href="papers/robotics-research-pipeline/index.html">Read the architecture and validation gates</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>A public skill link reaches a simulated arm</h3><p>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.</p><a href="papers/skillspace-arm-installation/index.html">Read the installation contract and arm replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>The offline cable environment is runnable</h3><p>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.</p><a href="papers/offline-cable-training-environment/index.html">Read the first product build and replay</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Contact accuracy meets runtime</h3><p>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.</p><a href="papers/fast-accurate-robot-training-simulation/index.html">Read the measurements</a></div></li><li><time datetime="2026-10-07">7 October 2026</time><div><h3>Compare the observation-to-skill thesis with prior work</h3><p>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.</p><a href="papers/grok-robotics-research-on-x/index.html">Read the comparison</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Five licensed POV excerpts processed</h3><p>Ten image calls completed. A checker accepted an object error and captions omitted visible state changes. The separate accuracy pilot remains unrun.</p><a href="papers/clef-and-grounded-video-observations/index.html">Read the feasibility run</a></div></li><li><time datetime="2026-10-06">6 October 2026</time><div><h3>Define the evidence and release questions</h3><p>Four baseline protocols and a Skillspace graduation proposal establish the questions to test. A proposed protocol is distinct from an executed experiment.</p><a href="papers/physical-skill-research-agenda/index.html">Read the agenda</a></div></li></ol></section><section class="journal-questions"><h2>Open questions</h2><ul>${topics.map(t => `<li><a href="topics/${t.id}/index.html">${escapeHtml(t.title)}</a> — ${escapeHtml(t.summary)}</li>`).join("")}</ul><p>Follow new publications through <a href="feed.xml">RSS</a> or inspect <a href="${REPOSITORY}">the source and revisions on GitHub</a>.</p></section>`;
   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 = `<?xml version="1.0" encoding="UTF-8"?>\n<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>DVIDIA Research</title><link>${ORIGIN}/</link><description>Research footprints on demonstrations, calibrated simulation and robot skills.</description><language>en</language><atom:link href="${ORIGIN}/feed.xml" rel="self" type="application/rss+xml"/>${catalog.publications.map(p => { const url = `${ORIGIN}/papers/${p.slug}/`; return `<item><title>${escapeHtml(p.title)}</title><link>${url}</link><guid isPermaLink="true">${url}</guid><pubDate>${new Date(`${p.date}T12:00:00Z`).toUTCString()}</pubDate><description>${escapeHtml(`${p.status}. ${p.abstract}`)}</description></item>`; }).join("")}</channel></rss>\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",
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+        "native_observation_count": 100,
+        "target_present_observation_count": 100,
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+            "max": 1.0856805211389336e-07
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+            "max": 2.1965565755177607e-09
+          },
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+            "max": 1.057792509081208e-06
+          },
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+          }
+        }
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+      "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 @@
+{
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+  "completion_decision": "Unmodified Jev Choice verdict; unknown is abstention.",
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+  "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.",
+  "timeout_s": 20.0,
+  "timeout_scope": "Per socket operation; not a hard wall-clock deadline.",
+  "trials": [
+    {
+      "case_id": "layout-02-wrong_position",
+      "condition": "rgb_telemetry",
+      "request_sha256": "4b2d21390d91a0e27ed7498506c325fb33f997108f0cd688646df0c8767b06b5"
+    },
+    {
+      "case_id": "layout-02-wrong_orientation",
+      "condition": "rgb",
+      "request_sha256": "cf2c595e98a0953986643451655b82b56ec9b6797f0385f172992a19a09e2902"
+    },
+    {
+      "case_id": "layout-01-moving_alias",
+      "condition": "rgb",
+      "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15"
+    },
+    {
+      "case_id": "layout-03-occluded",
+      "condition": "rgb",
+      "request_sha256": "6dc62c2ce15e117efcf3c31ebf6559225eec602a00771ca335893539e0ce7b17"
+    },
+    {
+      "case_id": "layout-03-moving_alias",
+      "condition": "rgb_telemetry",
+      "request_sha256": "562192137127c05e4d4f824bf7f12fd6f1885900aad2a486b8b2026602dc2e0b"
+    },
+    {
+      "case_id": "layout-03-complete",
+      "condition": "rgb_telemetry",
+      "request_sha256": "9cdeacbb6a7bfb89a52b1440577dc93a86581c29ac4a50445c64fbb9436a38cc"
+    },
+    {
+      "case_id": "layout-03-missing_target",
+      "condition": "rgb_telemetry",
+      "request_sha256": "a45d75d5ee6d6d2f3f98afba864906ff46b15fdececc6dfee1f03dffe06c816c"
+    },
+    {
+      "case_id": "layout-02-unreleased",
+      "condition": "rgb_telemetry",
+      "request_sha256": "ba1ab4131973a5d3833b50c1af81ec168dded4ea50209a2b9e88b6d64c07849a"
+    },
+    {
+      "case_id": "layout-04-complete",
+      "condition": "rgb",
+      "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855"
+    },
+    {
+      "case_id": "layout-03-wrong_orientation",
+      "condition": "rgb_telemetry",
+      "request_sha256": "2ea701cfba448a105267bbb27f8303456ca072d2d07a4a930d21034853fb262d"
+    },
+    {
+      "case_id": "layout-04-unreleased",
+      "condition": "rgb_telemetry",
+      "request_sha256": "6517dc9913124d034b3a54349b77f81feb05e049b0660a75c9b93d1443d0c425"
+    },
+    {
+      "case_id": "layout-03-occluded",
+      "condition": "rgb_telemetry",
+      "request_sha256": "0b9a08881ff8452447d301dc7e4379139afd0cda5b98a0ad7699e03a6e433c53"
+    },
+    {
+      "case_id": "layout-02-occluded",
+      "condition": "rgb",
+      "request_sha256": "1d6c918bd75bbb3d3544876daee9b4f4439c27f76d4a97126c1734d831add576"
+    },
+    {
+      "case_id": "layout-03-unreleased",
+      "condition": "rgb",
+      "request_sha256": "803f461156bfe7fc05d5fbbdab899f45c52b561e1700f4c8ba68873e3635d6dd"
+    },
+    {
+      "case_id": "layout-02-missing_target",
+      "condition": "rgb_telemetry",
+      "request_sha256": "6e3c7bda63e2584be56d9f5ffbcdf6228ba3f44c1d45e4d53f708f7e873d12da"
+    },
+    {
+      "case_id": "layout-01-wrong_orientation",
+      "condition": "rgb_telemetry",
+      "request_sha256": "5427dbcbe70b437b4949c2ef974905d4710ea0c994e8d92fd207d1017d494d6d"
+    },
+    {
+      "case_id": "layout-04-unreleased",
+      "condition": "rgb",
+      "request_sha256": "053c7b9d62bc72c6fccfee44a071cc0cb5c9fc4a09cef15f568ae958532d7e8e"
+    },
+    {
+      "case_id": "layout-01-unreleased",
+      "condition": "rgb",
+      "request_sha256": "29650676bc836d7065cc13f79ef3e1dbdadc7a139bfa45564dfd337c24f4918c"
+    },
+    {
+      "case_id": "layout-04-occluded",
+      "condition": "rgb_telemetry",
+      "request_sha256": "ec0eb371382c7fd7b35408483f2c9978fce56c17d2f6355bb4027ed68d4977af"
+    },
+    {
+      "case_id": "layout-04-occluded",
+      "condition": "rgb",
+      "request_sha256": "644de408fef8e6cb532ef8b5144f8d87742837887e4f05984b55341187a23a5b"
+    },
+    {
+      "case_id": "layout-01-complete",
+      "condition": "rgb_telemetry",
+      "request_sha256": "22c765474ea8183e3002b7f92eb27d13136b75d5e1515a007133afc05d459ec9"
+    },
+    {
+      "case_id": "layout-02-missing_target",
+      "condition": "rgb",
+      "request_sha256": "f73d1ad36b4b6537aec4ac4c603a04db290f1a02263ed7b48f65de7f8641edc9"
+    },
+    {
+      "case_id": "layout-02-wrong_object",
+      "condition": "rgb",
+      "request_sha256": "14d0e74adc997a0af6a29feb0bb8b5c7180fe519679936fa90265f896a87a350"
+    },
+    {
+      "case_id": "layout-02-moving_alias",
+      "condition": "rgb",
+      "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269"
+    },
+    {
+      "case_id": "layout-01-wrong_object",
+      "condition": "rgb_telemetry",
+      "request_sha256": "b9b3db8ea54fbadede8971e74c99fd7fecb513696e6a57c86f43b8a392b15cee"
+    },
+    {
+      "case_id": "layout-02-wrong_position",
+      "condition": "rgb",
+      "request_sha256": "0caffd3a26a3698840489fb4f7d7628a99d28dd969ab7e968b681dbf57380666"
+    },
+    {
+      "case_id": "layout-03-unreleased",
+      "condition": "rgb_telemetry",
+      "request_sha256": "8e094213a08d6ed860d1f868517b5a22417bb89f93029779f41f2d5974f02ad2"
+    },
+    {
+      "case_id": "layout-04-moving_alias",
+      "condition": "rgb_telemetry",
+      "request_sha256": "626df301847db14cad7060405b1875996cef9c3f6802e820440fc5cc3f47ac22"
+    },
+    {
+      "case_id": "layout-01-missing_target",
+      "condition": "rgb",
+      "request_sha256": "6680c0e5be92f50394933cd91f7fb856cedfc428d98983f245ba5719c920bb37"
+    },
+    {
+      "case_id": "layout-02-unreleased",
+      "condition": "rgb",
+      "request_sha256": "f6c0b2aa823c13f89f27aabcbb83816816e0f68cf026923a8d8916c170287a83"
+    },
+    {
+      "case_id": "layout-01-complete",
+      "condition": "rgb",
+      "request_sha256": "571f6b7928dbc9d2a6f2c0846975669e2f0f928efad97c69aafaa7cb56b66e15"
+    },
+    {
+      "case_id": "layout-02-occluded",
+      "condition": "rgb_telemetry",
+      "request_sha256": "f58a5417aaa6d0d5528b2c0c672e2fd6dfd9fb13080c382c080475529b938ca9"
+    },
+    {
+      "case_id": "layout-01-wrong_position",
+      "condition": "rgb",
+      "request_sha256": "fbdf1c3a54e1d8f3a51b5542528e4f621b5dd94e93db2228bc21828fbf8e4d85"
+    },
+    {
+      "case_id": "layout-03-wrong_position",
+      "condition": "rgb",
+      "request_sha256": "3908d00ae374e96f852d1e026bfd79ba6c1b2457611d9ae362168ae2ab1fd5b1"
+    },
+    {
+      "case_id": "layout-03-moving_alias",
+      "condition": "rgb",
+      "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c"
+    },
+    {
+      "case_id": "layout-01-moving_alias",
+      "condition": "rgb_telemetry",
+      "request_sha256": "0b5063721c28b72b90cd69cf3ff5eccd3d6a9b4e1f4b6e04d4acdeba9c5813e1"
+    },
+    {
+      "case_id": "layout-04-wrong_object",
+      "condition": "rgb",
+      "request_sha256": "e0143d52be87f1d72e4d955ea76e61e6fcdda047f9725766666cc6be808135ed"
+    },
+    {
+      "case_id": "layout-03-wrong_object",
+      "condition": "rgb_telemetry",
+      "request_sha256": "c48a75ef61b1c37da9f78fced2ad8c8fb64bb824764df2bafb810b5272ddb9b0"
+    },
+    {
+      "case_id": "layout-04-moving_alias",
+      "condition": "rgb",
+      "request_sha256": "63367143b05302c30c2cba9bf7c4bff0dafa2b2cff9280faceb1ceb80f2ef855"
+    },
+    {
+      "case_id": "layout-04-wrong_position",
+      "condition": "rgb",
+      "request_sha256": "691dbe273c38c698069ed53d87cc9b4bce9c714f66fd76e11d89569a7be6de69"
+    },
+    {
+      "case_id": "layout-03-wrong_orientation",
+      "condition": "rgb",
+      "request_sha256": "fd64d5be5c9fefcc4cbb3c3b42e73bafb28bf0fe169af7cb0dc2b64ec7b0bc32"
+    },
+    {
+      "case_id": "layout-01-wrong_orientation",
+      "condition": "rgb",
+      "request_sha256": "9aad7d221c1a2815360c5a7c7fc5d71ed186731fe9d727724868bedb5eb2042c"
+    },
+    {
+      "case_id": "layout-01-occluded",
+      "condition": "rgb_telemetry",
+      "request_sha256": "207e6f5519077f996117540549c91f9b242b6811633219a3d98f3141966939c6"
+    },
+    {
+      "case_id": "layout-04-missing_target",
+      "condition": "rgb_telemetry",
+      "request_sha256": "38c739b8b55cb6da3677e531b81e3868e16ced493b42c708c8e027fb8a653b71"
+    },
+    {
+      "case_id": "layout-04-wrong_orientation",
+      "condition": "rgb_telemetry",
+      "request_sha256": "cc06c03336fd573f3f93300c40f09356b1e6e25ce2e0e7f4c6f6c2b7e5b9e0eb"
+    },
+    {
+      "case_id": "layout-04-wrong_orientation",
+      "condition": "rgb",
+      "request_sha256": "c7abe212b11813b00ca8d4dcea622a21d1aff59d03c4a29bb21feb10939d813b"
+    },
+    {
+      "case_id": "layout-01-unreleased",
+      "condition": "rgb_telemetry",
+      "request_sha256": "27fd66094c3efe56842da714acc7be74fc97e09dea4ad67d7a4fe29ffa17d7a7"
+    },
+    {
+      "case_id": "layout-03-wrong_object",
+      "condition": "rgb",
+      "request_sha256": "3650b3cd6be734b696e8157da729427b1856d68eb905bce765cff3556c099814"
+    },
+    {
+      "case_id": "layout-02-complete",
+      "condition": "rgb",
+      "request_sha256": "aae2ccbe47bbe8cb832b48b231d1b1c152c92a5e96417227a5dd2b981a394269"
+    },
+    {
+      "case_id": "layout-02-complete",
+      "condition": "rgb_telemetry",
+      "request_sha256": "faa58fbc2678ab387f1f7f8b7c24aa781e0259cbf19a89a7459932ecb2a5d768"
+    },
+    {
+      "case_id": "layout-03-wrong_position",
+      "condition": "rgb_telemetry",
+      "request_sha256": "8a5b29305dec3510c2c7960557a0ef043eafb4237293f6b1507f3197242607fa"
+    },
+    {
+      "case_id": "layout-01-missing_target",
+      "condition": "rgb_telemetry",
+      "request_sha256": "150e2666d6e63b662012e973a5ab1e6e614d3492b7f324cdf3e9de9a3f0f4c98"
+    },
+    {
+      "case_id": "layout-01-occluded",
+      "condition": "rgb",
+      "request_sha256": "f653413e688093b5eec5d7b0597618d5946991f0921fb372650c7f562decfe95"
+    },
+    {
+      "case_id": "layout-04-wrong_object",
+      "condition": "rgb_telemetry",
+      "request_sha256": "b364766ccfff769c100afd83646101a3b4660191294982804942390f264d1cc3"
+    },
+    {
+      "case_id": "layout-01-wrong_position",
+      "condition": "rgb_telemetry",
+      "request_sha256": "a0ed0c8c2f72b936e34711c88f874179a2af286a17e8eaf9068dfe41bdf8e778"
+    },
+    {
+      "case_id": "layout-04-missing_target",
+      "condition": "rgb",
+      "request_sha256": "517d5035ec97f26d3d2c5ba307c827dac3f5e5a2cc831b82d648c94cdb6f2ef8"
+    },
+    {
+      "case_id": "layout-03-missing_target",
+      "condition": "rgb",
+      "request_sha256": "4a5cfb98445b511272f4eece16db8e09eedb075da0ce456fedc68d1280dda3f6"
+    },
+    {
+      "case_id": "layout-03-complete",
+      "condition": "rgb",
+      "request_sha256": "727e854ff9361e8e1948877d2d380e35c57ed6860a950f0e281a7f884a68725c"
+    },
+    {
+      "case_id": "layout-02-moving_alias",
+      "condition": "rgb_telemetry",
+      "request_sha256": "2b2cf534f89561d955526b1bb1db41baba75a591546dcd80349293a345ae7877"
+    },
+    {
+      "case_id": "layout-04-wrong_position",
+      "condition": "rgb_telemetry",
+      "request_sha256": "9a86c3e586b664e74763f17964bb1f41aae3dc2d3e938a2af44de193e52754d8"
+    },
+    {
+      "case_id": "layout-01-wrong_object",
+      "condition": "rgb",
+      "request_sha256": "57238b47f9786cb04bcc447130e5b4be6db4fe108092005392ea161d36e7b8f9"
+    },
+    {
+      "case_id": "layout-04-complete",
+      "condition": "rgb_telemetry",
+      "request_sha256": "63d8307d311a3bfd90ef515017c251ee82d3e4b714f7fd7bd716bf53b4d5974d"
+    },
+    {
+      "case_id": "layout-02-wrong_object",
+      "condition": "rgb_telemetry",
+      "request_sha256": "01b8450027128d7fe455f7d3b39c54fcac74a8fe25ff196aa0e15237b9f4f35f"
+    },
+    {
+      "case_id": "layout-02-wrong_orientation",
+      "condition": "rgb_telemetry",
+      "request_sha256": "b525b55a98301c6e1360cf7f82ec7a4215658fd7ba841fa75de19aa3ceea649e"
+    }
+  ]
+}
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+</style></head><body><a class="skip" href="#experiment">Skip to experiment</a><header class="masthead"><a class="brand" href="https://dvidia.org/" aria-label="DVIDIA home"><svg viewBox="0 0 64 64" aria-hidden="true"><rect width="64" height="64" rx="16" fill="#3c6cff"/><path d="M16 0h32c8.8 0 16 7.2 16 16v5C45 8 25 8 0 32V16C0 7.2 7.2 0 16 0Z" fill="#fff" opacity=".16"/><g fill="#f7f6f2"><rect x="8" y="20" width="4.6" height="24" rx="1.2" opacity=".28"/><rect x="14.1" y="20" width="4.6" height="24" rx="1.2" opacity=".38"/><rect x="20.2" y="20" width="4.6" height="24" rx="1.2" opacity=".48"/><rect x="26.3" y="20" width="4.6" height="24" rx="1.2" opacity=".58"/><rect x="32.4" y="20" width="4.6" height="24" rx="1.2" opacity=".68"/><rect x="38.5" y="20" width="4.6" height="24" rx="1.2" opacity=".78"/><rect x="44.6" y="20" width="4.6" height="24" rx="1.2" opacity=".9"/><rect x="50.7" y="20" width="4.6" height="24" rx="1.2" fill="#fff"/></g></svg><span>DVIDIA</span></a><nav class="top-links" aria-label="External resources"><a href="https://github.com/Dvidia-Inference/dvidia-training">Source code ↗</a><a href="https://research.dvidia.org/">Research notes ↗</a></nav></header>
+<main id="experiment"><p class="eyebrow">Robotics research · Image goals / 0.1</p><h1>Does it only look finished?</h1><p class="intro">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.</p><div id="run-state" class="run-state" role="status">Loading the recorded experiment.</div><noscript><p class="notice">Enable JavaScript to inspect individual cases. The complete public report remains available as JSON below.</p></noscript><div class="toolbar"><div><label for="condition">Model input</label><select id="condition"></select></div><p class="meta" id="run-meta"></p></div><dl id="metrics" class="metrics"></dl>
+<div class="workspace"><aside class="case-nav" aria-label="Benchmark cases"><h2>Inspect every case</h2><label class="meta" for="filter">Show</label><select id="filter" class="filter"><option value="all">All cases</option><option value="absent">Missing target</option><option value="incomplete">Physically incomplete</option><option value="false_complete">False completion</option><option value="error">Request errors</option><option value="not_run">Not run</option></select><div id="case-list" class="case-list"></div></aside><section aria-labelledby="case-title"><div class="case-heading"><h2 id="case-title"></h2><span id="case-category" class="meta"></span></div><div class="image-pair"><figure><img id="current" alt="Current simulated scene"><figcaption>Current observation</figcaption></figure><figure><img id="goal" alt="Requested goal arrangement"><figcaption>Goal reference</figcaption></figure></div><p id="case-notice" class="notice"></p><div class="comparison"><section><h3>Physical reference</h3><dl id="truth" class="facts"></dl><p class="note">Recorded simulator labels. A single image may not reveal hidden contact or sustained support.</p></section><section><h3>Jev judgment</h3><dl id="prediction" class="facts"></dl><p class="note">A remote research judgment. This page sends no images, runs no model and commands no robot.</p></section></div></section></div>
+<section class="fine"><h2>Count the misses. Keep the clock honest.</h2><p>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.</p><div class="table-wrap"><table><thead><tr><th>Decision check</th><th>Correct</th><th>Denominator</th></tr></thead><tbody id="decisions"></tbody></table></div><div class="table-wrap"><table><thead><tr><th>Latency scope</th><th>Timed requests</th><th>p50</th><th>p95</th><th>p99</th><th>Maximum</th></tr></thead><tbody id="latency"></tbody></table></div><p id="cost" class="note"></p><p>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.</p><p>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.</p><div class="downloads"><a href="report.json" download>Public results · JSON</a><a href="report-integrity.json" download>File checksums</a><a href="https://docs.typesafe.ai/images">Jev image documentation ↗</a></div></section><footer><p>Published by DVIDIA · <a href="mailto:hello@dvidia.org">hello@dvidia.org</a><br>Original report code: MIT. External models retain their own terms.</p><p><a href="https://huggingface.co/spaces/Dvidia/robot-simulation-lab">Hugging Face</a> · <a href="https://x.com/iammrriver">@iammrriver</a></p></footer></main>
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{"case_id": "layout-04-missing_target", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-wrong_orientation", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-wrong_orientation", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-unreleased", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": 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null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-01-wrong_object", "condition": "rgb", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-04-complete", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-wrong_object", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, "p_present": null, "p_complete": null, "verdict": null, "timing_ms": {}, "estimated_input_cost_usd": null}, {"case_id": "layout-02-wrong_orientation", "condition": "rgb_telemetry", "status": "not_run", "decision": {"present": null, "complete": null}, 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"attempted": 0, "responses": 0, "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."}</script><script>
+"use strict";
+const data=JSON.parse(document.getElementById("public-data").textContent),byId=id=>document.getElementById(id),human=value=>String(value).replaceAll("_"," ").replaceAll("-"," "),labelCondition=value=>({rgb:"Images only",rgb_telemetry:"Images + measured state"}[value]||human(value)),success=new Set(["ok","success","completed"]),pending=new Set(["pending","not_run","not-run","unrun","missing_api_key","missing_credentials"]);let selected=data.cases[0].id;
+const text=(id,value)=>{byId(id).textContent=value},bool=value=>value===true?"Yes":value===false?"No":"Unreported",prob=value=>value===null?"Unreported":(value*100).toFixed(1)+"%",ms=value=>value===null?"Unreported":value.toFixed(1)+" ms",rowFor=(id,condition)=>data.results.find(row=>row.case_id===id&&row.condition===condition),condition=()=>byId("condition").value;
+for(const value of data.conditions){const option=document.createElement("option");option.value=value;option.textContent=labelCondition(value);byId("condition").append(option)}
+function pairs(id,values){const node=byId(id);node.replaceChildren();for(const [key,value] of values){const dt=document.createElement("dt"),dd=document.createElement("dd");dt.textContent=key;dd.textContent=value;node.append(dt,dd)}}
+function metric(title,value,detail){const wrap=document.createElement("div");wrap.className="metric";const dt=document.createElement("dt"),dd=document.createElement("dd"),small=document.createElement("small");dt.textContent=title;dd.textContent=value;small.textContent=detail;wrap.append(dt,dd,small);return wrap}
+function renderSummary(){
+const summary=data.summary.find(row=>row.condition===condition()),a=summary.observability_aware_accuracy,f=summary.false_completion,p=summary.false_presence;
+byId("metrics").replaceChildren(
+metric("Observability-aware judgments",summary.attempted&&a.planned?`${a.correct} / ${a.planned}`:"Not measured",`${summary.responses} responses · ${summary.completion_abstentions} abstentions · ${summary.errors} errors · ${summary.not_run} not run`),
+metric("Physical false completion",summary.attempted&&f.planned_incomplete?`${f.count} / ${f.planned_incomplete}`:"Not measured",`${f.count}/${f.valid_incomplete} valid responses · ${f.answered_incomplete}/${f.planned_incomplete} explicit-decision coverage`),
+metric("False target presence",summary.attempted&&p.planned_absent?`${p.count} / ${p.planned_absent}`:"Not measured",`${p.count}/${p.valid_absent} valid responses · ${p.answered_absent}/${p.planned_absent} explicit-decision coverage`),
+metric("Response latency · p95",ms(summary.successful_response_latency_ms.p95),`${summary.successful_response_latency_ms.n} timed successful responses`));
+text("run-meta",`${data.model} · ${data.cases.length} synthetic cases · ${summary.attempted}/${summary.planned} attempted in this condition`);
+const total=data.summary.reduce((n,s)=>n+s.attempted,0);text("run-state",total?`Recorded benchmark · ${total} requests attempted. Every planned case remains visible, including errors and missing responses.`:"Jev has not been run. These are prepared simulation cases; model accuracy and service latency have not been measured.");
+const decisionBody=byId("decisions");decisionBody.replaceChildren();for(const [name,count,denominator,scope] of [["Known verdicts",summary.known_verdict_accuracy.correct,summary.known_verdict_accuracy.planned,"planned cases with an observable verdict"],["Justified abstention",summary.ambiguous_abstention.correct,summary.ambiguous_abstention.planned,"planned ambiguous cases"],["Selective physical agreement",summary.completion_accuracy.correct,summary.completion_accuracy.answered,"explicit answered decisions"]]){const tr=document.createElement("tr");for(const value of [name,summary.attempted&&denominator?`${count} / ${denominator}`:"Not measured",scope]){const td=document.createElement("td");td.textContent=value;tr.append(td)}decisionBody.append(tr)}
+text("cost",`Known-usage estimated input cost: ${summary.known_usage_estimated_input_cost_usd===null?"unreported":"$"+summary.known_usage_estimated_input_cost_usd.toFixed(6)}. ${summary.unknown_cost_attempts} attempted requests have unknown cost. This estimate is not a billing receipt.`);
+const tbody=byId("latency");tbody.replaceChildren();for(const [name,values] of [["All timed attempts",summary.all_attempt_latency_ms],["Successful responses",summary.successful_response_latency_ms]]){const tr=document.createElement("tr");for(const value of [name,values.n,ms(values.p50),ms(values.p95),ms(values.p99),ms(values.max)]){const td=document.createElement("td");td.textContent=value;tr.append(td)}tbody.append(tr)}}
+function visible(caseRow){const row=rowFor(caseRow.id,condition()),filter=byId("filter").value;return filter==="all"||filter==="absent"&&caseRow.ground_truth.present===false||filter==="incomplete"&&caseRow.ground_truth.complete===false||filter==="false_complete"&&caseRow.ground_truth.complete===false&&row&&success.has(row.status)&&row.decision.complete===true||filter==="error"&&row&&!success.has(row.status)&&!pending.has(row.status)||filter==="not_run"&&(!row||pending.has(row.status))}
+function renderCases(){const shown=data.cases.filter(visible),list=byId("case-list");list.replaceChildren();if(!shown.length){const empty=document.createElement("p");empty.className="empty";empty.textContent="No cases match this filter.";list.append(empty)}if(shown.length&&!shown.some(row=>row.id===selected))selected=shown[0].id;for(const caseRow of shown){const row=rowFor(caseRow.id,condition()),button=document.createElement("button"),small=document.createElement("small");button.type="button";button.setAttribute("aria-pressed",String(caseRow.id===selected));button.textContent=human(caseRow.id);small.textContent=row?human(row.status):"Not run";if(row&&!success.has(row.status)&&!pending.has(row.status))small.className="error";button.append(small);button.addEventListener("click",()=>{selected=caseRow.id;renderCases()});list.append(button)}renderCase()}
+function renderCase(){
+const current=data.cases.find(row=>row.id===selected),row=rowFor(current.id,condition()),expected=current.expected_verdict[condition()],measured=row&&success.has(row.status);
+text("case-title",human(current.id));text("case-category",human(current.category));byId("current").src=current.current_image;byId("goal").src=current.goal_image;byId("current").alt=`Current simulated scene for ${human(current.id)}`;byId("goal").alt=`Goal reference for ${human(current.id)}`;
+pairs("truth",[["Target present",bool(current.ground_truth.present)],["Physically complete",bool(current.ground_truth.complete)],["Expected from this input",expected?human(expected):"Unreported"]]);
+pairs("prediction",[["Request status",row?human(row.status):"Not run"],["Target present",measured?bool(row.decision.present):"Unmeasured"],["Completion verdict",measured&&row.verdict?human(row.verdict):"Unmeasured"],["Presence probability",measured?prob(row.p_present):"Unmeasured"],["Completion probability",measured?prob(row.p_complete):"Unmeasured"],["Total latency",row&&!pending.has(row.status)?ms(row.timing_ms.total_ms??null):"Unmeasured"]]);
+let message="No Jev result is recorded for this case and input condition.";
+if(row&&success.has(row.status)){
+if(expected)message=row.verdict===expected?"The model judgment agrees with the frozen expected verdict for this input.":"Judgment disagreement: the model did not return the frozen expected verdict for this input.";
+else message=row.decision.complete===null?"The model did not make an explicit completion decision.":current.ground_truth.complete===null?"No physical completion reference was reported.":row.decision.complete!==current.ground_truth.complete?"Completion disagreement: the model judgment does not match the physical reference.":"Completion judgment agrees with the physical reference for this case.";
+if(expected==="unknown")message+=" This image does not establish every physical requirement; unknown is the expected answer.";
+if(current.ground_truth.complete===false&&row.decision.complete===true)message+=" False completion: the physical task is incomplete.";
+if(current.ground_truth.present===false&&row.decision.present===true)message+=" The model reported a target that is absent.";
+}else if(row&&!pending.has(row.status))message="The request failed. This case remains in the planned cohort and error count.";
+text("case-notice",message)}
+byId("condition").addEventListener("change",()=>{renderSummary();renderCases()});byId("filter").addEventListener("change",renderCases);renderSummary();renderCases();
+</script></body></html>
\ No newline at end of file
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json
new file mode 100644
index 0000000..6f5448a
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/integrity.json
@@ -0,0 +1,340 @@
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+  ]
+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/licenses/agilex_piper/LICENSE 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": {
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+    "moving_alias": 4,
+    "occluded": 4,
+    "unreleased": 4,
+    "wrong_object": 4,
+    "wrong_orientation": 4,
+    "wrong_position": 4
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+  "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": [
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+      "byte_identical": true,
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+      "pixel_identical_to": "layout-02-complete",
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+      "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
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+    {
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+      "pixel_identical_to": "layout-04-complete",
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+      "png_bytes": 77762,
+      "byte_identical": true,
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+      "category": "complete",
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+      "yaw_error_rad": 2.1965561156353097e-09,
+      "table_support_force_n": 0.2942999990014351,
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+      "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",
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+      "complete": false,
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+      "supported": false,
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+      "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",
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+      "linear_speed_m_s": null,
+      "angular_speed_rad_s": null,
+      "native_history_samples": 100,
+      "expected_verdict": {
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+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-wrong_position",
+      "category": "wrong_position",
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+      "complete": false,
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+      "native_history_samples": 100,
+      "expected_verdict": {
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+        "rgb_telemetry": "incomplete"
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+    },
+    {
+      "id": "layout-01-wrong_orientation",
+      "category": "wrong_orientation",
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-01-unreleased",
+      "category": "unreleased",
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+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
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+      "category": "moving_alias",
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+      "expected_verdict": {
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+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
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+      "category": "occluded",
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+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
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+      "category": "complete",
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+      "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,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.294299999001435,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 9.860076016164131e-10,
+      "angular_speed_rad_s": 2.7627745907984584e-09,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    },
+    {
+      "id": "layout-04-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-04-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-04-wrong_position",
+      "category": "wrong_position",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": true,
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.29429999900143494,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 9.86007605782391e-10,
+      "angular_speed_rad_s": 2.762774742684285e-09,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-wrong_orientation",
+      "category": "wrong_orientation",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": true,
+      "position_error_m": 1.0856805194707237e-07,
+      "yaw_error_rad": 1.57079632459834,
+      "table_support_force_n": 0.294299999001435,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 9.86007598437561e-10,
+      "angular_speed_rad_s": 2.7627747672417554e-09,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "incomplete",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-unreleased",
+      "category": "unreleased",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": false,
+      "position_error_m": 0.0010491825564488413,
+      "yaw_error_rad": 0.03690074331083231,
+      "table_support_force_n": 0.3144476609893772,
+      "robot_contact_force_n": 0.790406083741399,
+      "linear_speed_m_s": 0.00010064949189093267,
+      "angular_speed_rad_s": 0.002606933470747755,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-moving_alias",
+      "category": "moving_alias",
+      "present": true,
+      "complete": false,
+      "physical_conditions_dwell_s": 0.0,
+      "supported": true,
+      "released": true,
+      "position_error_m": 1.0856805211389336e-07,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.3623192350084462,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 0.12,
+      "angular_speed_rad_s": 2.7627745907984584e-09,
+      "native_history_samples": 0,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "incomplete"
+      }
+    },
+    {
+      "id": "layout-04-occluded",
+      "category": "occluded",
+      "present": true,
+      "complete": true,
+      "physical_conditions_dwell_s": 0.2,
+      "supported": true,
+      "released": true,
+      "position_error_m": 1.0856805211389336e-07,
+      "yaw_error_rad": 2.1965565755177607e-09,
+      "table_support_force_n": 0.294299999001435,
+      "robot_contact_force_n": 0.0,
+      "linear_speed_m_s": 9.860076016164131e-10,
+      "angular_speed_rad_s": 2.7627745907984584e-09,
+      "native_history_samples": 100,
+      "expected_verdict": {
+        "rgb": "unknown",
+        "rgb_telemetry": "complete"
+      }
+    }
+  ]
+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json
new file mode 100644
index 0000000..2be4fb8
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/protocol.json
@@ -0,0 +1,146 @@
+{
+  "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
+  },
+  "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 @@
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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
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+  "schema": "dvidia.image-goal-publication-provenance/v1",
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/publication-integrity.json
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index 0000000..1ef33fa
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json
new file mode 100644
index 0000000..74d01bc
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report-integrity.json
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json
new file mode 100644
index 0000000..1ab6105
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/report.json
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+  "status": "not_run",
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+    {
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+    {
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+    },
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+      "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": [
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+      "case_id": "layout-02-wrong_position",
+      "condition": "rgb_telemetry",
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+        "complete": null,
+        "present": null
+      },
+      "error": {
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+        "message": "TYPESAFE_API_KEY is not configured"
+      },
+      "estimated_input_cost_usd": null,
+      "http_status": null,
+      "images": {
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+          "sha256": "f31324821c1b69ed64bc7776437f292b8d20e87e2aa237135cb2b084fd6f0e76",
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+      "presence": "unknown",
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+            "type": "image"
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+            "part": "goal",
+            "type": "image"
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+        "model": "jev-1.14.0",
+        "questions": {
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+            "criteria": {
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+              "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"
+          }
+        },
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+            "type": "noul"
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+            "criteria": {
+              "false": "The target object is absent from the current scene.",
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+            "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"
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+              "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.",
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+            "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"
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+        "questions": {
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+            "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.",
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+      "verdict": "unknown",
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+        "message": "TYPESAFE_API_KEY is not configured"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "type": "noul"
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+              "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`."
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+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+          "presence": {
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+            "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"
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+              "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`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "type": "noul"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "type": "noul"
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+          "verdict": {
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+              "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+    {
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+        "kind": "missing_credentials",
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+            "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.",
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+          },
+          "presence": {
+            "criteria": {
+              "false": "The target object is absent from the current scene.",
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+            "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"
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+          "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`."
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+            "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"
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+    {
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+        "kind": "missing_credentials",
+        "message": "TYPESAFE_API_KEY is not configured"
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+      "images": {
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+          "bytes": 71367,
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+            "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.",
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+          "presence": {
+            "criteria": {
+              "false": "The target object is absent from the current scene.",
+              "true": "The target object is present in the current scene."
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+            "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"
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+          "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"
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+            "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"
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+          "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"
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+          "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"
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+              "false": "At least one physical requirement explicitly defined in `task` is not satisfied.",
+              "true": "All physical requirements explicitly defined in `task` are satisfied."
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+            "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"
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+          "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"
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+          "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."
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+      "request_bytes": 163219,
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+      "verdict": "unknown",
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+    {
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+        "message": "TYPESAFE_API_KEY is not configured"
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+      "estimated_input_cost_usd": null,
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+      "images": {
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+            "type": "noul"
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+            "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`."
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+            "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.",
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+            "type": "noul"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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"
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+            "type": "noul"
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+              "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`."
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+            "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"
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+            "type": "noul"
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+              "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`."
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+            "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"
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+            "type": "noul"
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+          "presence": {
+            "criteria": {
+              "false": "The target object is absent from the current scene.",
+              "true": "The target object is present in the current scene."
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+            "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"
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+          "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"
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+          "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."
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "true": "The target object is present in the current scene."
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+            "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"
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+              "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.",
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+            "type": "noul"
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+              "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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"
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+              "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`."
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+            "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.",
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+            "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"
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+              "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`."
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+            "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.",
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+            "type": "noul"
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+              "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"
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+            "type": "noul"
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+              "true": "The target object is present in the current scene."
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+            "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"
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+              "unknown": "No observed failure is established, but the available evidence cannot establish every physical requirement in `task`."
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+            "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.",
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+        "message": "TYPESAFE_API_KEY is not configured"
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+      "images": {
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+            "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."
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+            "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"
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+          "verdict": {
+            "criteria": {
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+              "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`."
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+            "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"
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+            "target_present_observation_count": 0,
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+          "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."
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+            "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."
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+            "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"
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+          "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"
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+  "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
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-complete.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json
new file mode 100644
index 0000000..d671eba
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-moving_alias.json
@@ -0,0 +1,94 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json
new file mode 100644
index 0000000..7eff49c
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-occluded.json
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json
new file mode 100644
index 0000000..6fd7a5e
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json
new file mode 100644
index 0000000..29b5cc1
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_object.json
@@ -0,0 +1,1274 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json
new file mode 100644
index 0000000..0341181
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_orientation.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json
new file mode 100644
index 0000000..967c6bf
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-01-wrong_position.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json
new file mode 100644
index 0000000..07d6583
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-complete.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json
new file mode 100644
index 0000000..7e3f3c3
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-moving_alias.json
@@ -0,0 +1,94 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json
new file mode 100644
index 0000000..07d6583
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-occluded.json
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json
new file mode 100644
index 0000000..226b7ed
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json
new file mode 100644
index 0000000..d0e3226
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_object.json
@@ -0,0 +1,1274 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json
new file mode 100644
index 0000000..dbb96fd
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_orientation.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json
new file mode 100644
index 0000000..5d2daf1
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-02-wrong_position.json
@@ -0,0 +1,3395 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json
new file mode 100644
index 0000000..1e9a062
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-complete.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-missing_target.json
@@ -0,0 +1,1261 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json
new file mode 100644
index 0000000..e0cb093
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-moving_alias.json
@@ -0,0 +1,94 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json
new file mode 100644
index 0000000..1e9a062
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-occluded.json
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json
new file mode 100644
index 0000000..bec6399
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json
new file mode 100644
index 0000000..d14b9fc
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_object.json
@@ -0,0 +1,1274 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json
new file mode 100644
index 0000000..d7a4ba9
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_orientation.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json
new file mode 100644
index 0000000..dfbd2ab
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-03-wrong_position.json
@@ -0,0 +1,3395 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json
new file mode 100644
index 0000000..e6ab29c
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-complete.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json
new file mode 100644
index 0000000..fc6e422
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-missing_target.json
@@ -0,0 +1,1261 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json
new file mode 100644
index 0000000..ee6f40e
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-moving_alias.json
@@ -0,0 +1,94 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json
new file mode 100644
index 0000000..e6ab29c
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-occluded.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json
new file mode 100644
index 0000000..3f884f9
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-unreleased.json
@@ -0,0 +1,4203 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json
new file mode 100644
index 0000000..9802fd3
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_object.json
@@ -0,0 +1,1274 @@
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diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json
new file mode 100644
index 0000000..5c3b39e
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_orientation.json
@@ -0,0 +1,3395 @@
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+}
diff --git a/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json
new file mode 100644
index 0000000..d820b7f
--- /dev/null
+++ b/topics/physical-grounding/runs/2026-10-18-image-goal-jev-v01/lab/states/layout-04-wrong_position.json
@@ -0,0 +1,3395 @@
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+          "normal_force_n": 0.07422444022905758
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.13000010836562856,
+        -0.00010773875709819398
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.1965564367398827e-09,
+      "linear_speed_m_s": 1.4025099190536022e-09,
+      "angular_speed_rad_s": 3.876935729646871e-09,
+      "table_support_force_n": 0.29429999857937106,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.0800216791347261
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.0729025881043974
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.06715129108210273
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07422444025814484
+        }
+      ],
+      "instantaneous_conditions_met": false
+    },
+    {
+      "target_present": true,
+      "target_position_m": [
+        0.5899999933733543,
+        -0.1300001083656286,
+        -0.00010773875448404371
+      ],
+      "position_error_m": 0.16999999337338886,
+      "yaw_error_rad": 2.196556492251034e-09,
+      "linear_speed_m_s": 1.3070751376987822e-09,
+      "angular_speed_rad_s": 3.6230405882734287e-09,
+      "table_support_force_n": 0.2942999986760867,
+      "robot_contact_force_n": 0.0,
+      "contacts": [
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.08002167915673436
+        },
+        {
+          "other_geom": "workstation_table_top",
+          "normal_force_n": 0.07290258812444228
+        },
+        {
+          "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/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())