Publish synthetic DVIDIA training pilot and evidence

LICENSE

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+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.

README.md

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+---
+license: mit
+tags:
+  - video
+  - robotics
+  - simulation
+  - synthetic
+  - dvidia-training
+---
+
+![DVIDIA Training](assets/branding/github-header.png)
+
+# DVIDIA Training: synthetic examples
+
+Two authored datasets for reproducing the DVIDIA Training v0.1.0 CPU pipeline: **moving-disc videos for visual prediction** and **native simulation recordings with numerical movement labels**. These are software research fixtures. They contain no human footage, shoe demonstrations, physical robot recordings or calibrated camera perception.
+
+[Training artifacts and measurements](https://huggingface.co/Dvidia/dvidia-training-pilot) · [Demo](https://huggingface.co/spaces/Dvidia/dvidia-training) · [Source and installation](https://github.com/Dvidia-Inference/dvidia-training)
+
+## Contents
+
+| Dataset | Source | Scale | Supervision |
+| --- | --- | --- | --- |
+| `visual/source/` | Procedurally drawn moving-disc MP4 videos | 10 recordings, 6 seconds each; 60 seconds total | Visual next-frame targets; no action labels |
+| `native/source/` | Ten separately executed native MuJoCo placement recordings with schematic MP4 videos | 10 recordings; 116.5 seconds total | Timestamped privileged simulator state and authored inverse-kinematics joint targets |
+
+Videos are H.264, 160 × 128 RGB schematics sampled at 10 Hz. The native videos depict a simulated six-joint arm and rigid box; they are not camera renderings. The native control labels were collected at 20 ms control timestamps when an authored target was requested. Source seeds are `8000` through `8009`.
+
+Each source directory contains `skillspace.training.json`, `example-receipt.json` and `videos/`. Native source also contains `actions/`, `evidence/` and `task.skill.json`. Each native sidecar contains a timestamp, 12 context values, 6 bounded goal/orientation error values and 6 joint-delta targets per sample. Its feature contract is:
+
+```text
+q6_object_minus_tcp3_target_minus_tcp3__capped_goal_delta3_upright_orientation_delta3_v1
+```
+
+The action labels come from simulator state and authored targets, not from recovering actions from pixels. Media, sample and evidence hashes record declared consistency; the alignment declaration is not an independent attestation. The prepared intake receipt retains `actions_validation: "unvalidated_numeric_contract"`; the later movement trainer checks the strict sidecar contract, hashes and declared alignment. Neither stage independently attests the underlying evidence.
+
+`archives/visual-skillspace.zip` and `archives/native-skillspace.zip` preserve the original source exports. `visual/prepared/` and `native/prepared/` preserve the measured `dataset.json`, `source_manifest.json` and every referenced `data/…` video or action sidecar from the published evidence. These prepared datasets can be supplied directly to DVIDIA Training; their recording split and media hashes remain inspectable.
+
+## Recorded splits
+
+Both datasets use seed `17` and connected groups formed from shared recording IDs, session IDs, shoe-pair IDs when present, and exact video hashes. In these examples, each recording has its own declared recording/session group. The protocol's target ratios are 0.70/0.15/0.15; the recorded allocation is 7/2/1 groups.
+
+| Split | Recording IDs in each dataset | Visual duration | Native movement samples |
+| --- | --- | --- | --- |
+| Training | `example-01`, `02`, `04`, `05`, `07`, `08`, `09` | 42 seconds | 4,029 |
+| Development | `example-00`, `03` | 12 seconds | 1,154 |
+| Test | `example-06` | 6 seconds | 621 |
+
+Abbreviated IDs in the table retain the `example-` prefix. Training fits weights, development selects ridge regularization, and test is scored after selection. The visual learner samples 12 frames per recording: 84/24/12 frames and 77/22/11 within-recording transitions across training/development/test.
+
+The ten native recordings total 5,804 movement samples. Samples within a recording are correlated. Separate recording groups and supplier-declared `complete` flags do not establish independent task trials, novelty or a sufficient number of demonstrations. `example-06` is also the exact native scene used for the separate one-scene qualification; that qualification adds no new held-out scene.
+
+## Download and reproduce
+
+This repository is an archive and JSON/MP4 dataset, without a packaged Hugging Face `datasets.load_dataset` builder. Download it with `huggingface_hub`, then use DVIDIA Training's source intake. The following requires Python 3.12+ and system FFmpeg, including `ffprobe`.
+
+```sh
+python -m pip install huggingface_hub
+python -m pip install https://github.com/Dvidia-Inference/dvidia-training/releases/download/v0.1.0/dvidia_training-0.1.0-py3-none-any.whl
+```
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="Dvidia/dvidia-training-examples",
+    repo_type="dataset",
+    local_dir="examples",
+)
+```
+
+Train and verify the visual result, using a fresh output directory:
+
+```sh
+dvidia-train run examples/archives/visual-skillspace.zip --seed 17 --output runs/visual
+dvidia-train inspect runs/visual
+```
+
+To reuse the published prepared split and referenced media, replace the source argument with `examples/visual/prepared/dataset.json` (or `examples/native/prepared/dataset.json` for the native branch).
+
+For the native movement branch and candidate capsule, add the optional simulation engine and supply the explicit task source:
+
+```sh
+python -m pip install mujoco==3.15.0
+dvidia-train run examples/archives/native-skillspace.zip --seed 17 --task-source examples/native/source/task.skill.json --output runs/native
+dvidia-train inspect runs/native
+```
+
+Software installation and repository download require network access. Training on the downloaded files is offline by default. Fitting and artifact inspection do not perform closed-loop qualification. See the [native pilot procedure](https://github.com/Dvidia-Inference/dvidia-training/blob/v0.1.0/benchmarks/native_pilot.py) for the separate collect, train, install and exact-scene test. Numerical reproducibility can depend on installed decoders and numerical libraries.
+
+## Provenance, license and intended use
+
+DVIDIA contributors authored the source generator and the synthetic recordings. These files originate in the [2026-10-08 footage-pipeline release](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-08-footage-pipeline-v1/README.md). The [release ledger](https://research.dvidia.org/downloads/topics/physical-grounding/runs/2026-10-08-footage-pipeline-v1/release-ledger.json) declares MIT code and footage licenses and records an initial export-integration failure before the completed runs. The repository's `LICENSE` preserves the MIT copyright and permission notice.
+
+Use these examples to inspect video intake, provenance records, grouped splits, small CPU fitting and simulation-only movement supervision. Their simple imagery, narrow authored trajectories and privileged numerical labels do not represent human behavior or the variety of real robot tasks. No human-video action bridge, shoe-organizing skill, unseen-scene generalization or physical robot qualification has been demonstrated. Reported model metrics and the single native qualification are documented separately in the [model card](https://huggingface.co/Dvidia/dvidia-training-pilot).

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native/prepared/source_manifest.json

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+{"description":"Encoded schematic test fixture; no human footage, shoe task, physical arm or learned perception bridge.","kind":"dvidia.skillspace-training-source","license":"MIT","media":[{"actions_path":"actions/example-00.json","complete":true,"id":"example-00","path":"videos/example-00.mp4","session_id":"synthetic-session-00","source_recording_id":"synthetic-recording-00"},{"actions_path":"actions/example-01.json","complete":true,"id":"example-01","path":"videos/example-01.mp4","session_id":"synthetic-session-01","source_recording_id":"synthetic-recording-01"},{"actions_path":"actions/example-02.json","complete":true,"id":"example-02","path":"videos/example-02.mp4","session_id":"synthetic-session-02","source_recording_id":"synthetic-recording-02"},{"actions_path":"actions/example-03.json","complete":true,"id":"example-03","path":"videos/example-03.mp4","session_id":"synthetic-session-03","source_recording_id":"synthetic-recording-03"},{"actions_path":"actions/example-04.json","complete":true,"id":"example-04","path":"videos/example-04.mp4","session_id":"synthetic-session-04","source_recording_id":"synthetic-recording-04"},{"actions_path":"actions/example-05.json","complete":true,"id":"example-05","path":"videos/example-05.mp4","session_id":"synthetic-session-05","source_recording_id":"synthetic-recording-05"},{"actions_path":"actions/example-06.json","complete":true,"id":"example-06","path":"videos/example-06.mp4","session_id":"synthetic-session-06","source_recording_id":"synthetic-recording-06"},{"actions_path":"actions/example-07.json","complete":true,"id":"example-07","path":"videos/example-07.mp4","session_id":"synthetic-session-07","source_recording_id":"synthetic-recording-07"},{"actions_path":"actions/example-08.json","complete":true,"id":"example-08","path":"videos/example-08.mp4","session_id":"synthetic-session-08","source_recording_id":"synthetic-recording-08"},{"actions_path":"actions/example-09.json","complete":true,"id":"example-09","path":"videos/example-09.mp4","session_id":"synthetic-session-09","source_recording_id":"synthetic-recording-09"}],"schema_version":1,"synthetic":true,"task":"Synthetic native rigid-box placement"}

native/source/example-receipt.json

@@ -0,0 +1 @@
+{"clips":10,"episodes":[{"id":"example-00","reason":"success","simulation_seconds":11.789999999998905,"success":true},{"id":"example-01","reason":"success","simulation_seconds":11.548999999999038,"success":true},{"id":"example-02","reason":"success","simulation_seconds":11.34799999999915,"success":true},{"id":"example-03","reason":"success","simulation_seconds":11.266999999999195,"success":true},{"id":"example-04","reason":"success","simulation_seconds":11.325999999999162,"success":true},{"id":"example-05","reason":"success","simulation_seconds":11.683999999998964,"success":true},{"id":"example-06","reason":"success","simulation_seconds":12.41099999999856,"success":true},{"id":"example-07","reason":"success","simulation_seconds":11.72799999999894,"success":true},{"id":"example-08","reason":"success","simulation_seconds":11.506999999999062,"success":true},{"id":"example-09","reason":"success","simulation_seconds":11.345999999999151,"success":true}],"native_arm":true,"schema_version":1,"synthetic":true}

native/source/skillspace.training.json

@@ -0,0 +1 @@
+{"description":"Encoded schematic test fixture; no human footage, shoe task, physical arm or learned perception bridge.","kind":"dvidia.skillspace-training-source","license":"MIT","media":[{"actions_path":"actions/example-00.json","complete":true,"id":"example-00","path":"videos/example-00.mp4","session_id":"synthetic-session-00","source_recording_id":"synthetic-recording-00"},{"actions_path":"actions/example-01.json","complete":true,"id":"example-01","path":"videos/example-01.mp4","session_id":"synthetic-session-01","source_recording_id":"synthetic-recording-01"},{"actions_path":"actions/example-02.json","complete":true,"id":"example-02","path":"videos/example-02.mp4","session_id":"synthetic-session-02","source_recording_id":"synthetic-recording-02"},{"actions_path":"actions/example-03.json","complete":true,"id":"example-03","path":"videos/example-03.mp4","session_id":"synthetic-session-03","source_recording_id":"synthetic-recording-03"},{"actions_path":"actions/example-04.json","complete":true,"id":"example-04","path":"videos/example-04.mp4","session_id":"synthetic-session-04","source_recording_id":"synthetic-recording-04"},{"actions_path":"actions/example-05.json","complete":true,"id":"example-05","path":"videos/example-05.mp4","session_id":"synthetic-session-05","source_recording_id":"synthetic-recording-05"},{"actions_path":"actions/example-06.json","complete":true,"id":"example-06","path":"videos/example-06.mp4","session_id":"synthetic-session-06","source_recording_id":"synthetic-recording-06"},{"actions_path":"actions/example-07.json","complete":true,"id":"example-07","path":"videos/example-07.mp4","session_id":"synthetic-session-07","source_recording_id":"synthetic-recording-07"},{"actions_path":"actions/example-08.json","complete":true,"id":"example-08","path":"videos/example-08.mp4","session_id":"synthetic-session-08","source_recording_id":"synthetic-recording-08"},{"actions_path":"actions/example-09.json","complete":true,"id":"example-09","path":"videos/example-09.mp4","session_id":"synthetic-session-09","source_recording_id":"synthetic-recording-09"}],"schema_version":1,"synthetic":true,"task":"Synthetic native rigid-box placement"}

native/source/task.skill.json

@@ -0,0 +1,13 @@
+{
+  "vdia": "0",
+  "skill": "place_cup",
+  "version": "0.3.0",
+  "task": "Set a cup on the mark.",
+  "rail": "physical",
+  "demos": 1,
+  "format": "lerobot/v3",
+  "codebase_version": "v3.0",
+  "robot_type": "dvidia.wrist_room",
+  "cameras": ["ego", "room"],
+  "residual": {"claimer": 0.5, "contributors": 0.5, "book": "not-live"}
+}