DVIDIA task environments v02 41f1a82e0e98863ced5ca0bbc95380eadc3d2301d12e93afa1eaa9b3e1fd1d8c dataset 0

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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
+language:
+- en
+tags:
+- robotics
+- simulation
+- openusd
+- mujoco
+- agilex-piper
+- dvidia
+pretty_name: DVIDIA task environments v0.2
+size_categories:
+- n<1K
+---
+
+# DVIDIA task environments v0.2
+
+Reusable local environment recipes for a pinned native PiPER arm, a five-collider open bin, and up to four original rigid object instances. A rigid shoe surrogate is compound box geometry; it is not a measured or deformable shoe. All dynamics parameters are authored assumptions.
+
+The frozen passive benchmark retains 6 positive native scenes, 6 negative completion fixtures, and 4 invalid-layout prechecks. All 12 native outcomes matched their declarations; all 4 invalid layouts were rejected. A separate read-only audit reconstructed 13,262 recorded 1 ms state/goal samples and 1,337 saved replay frames with zero consistency mismatches. These counts measure scene and verifier checks, not a learned robot manipulation success rate.
+
+[Open the environment studio and recorded benchmark](https://dvidia-robot-simulation-lab.static.hf.space/task-environments/v02/index.html) · [Source and Terminal tools](https://github.com/Dvidia-Inference/dvidia-training)
+
+Download `task-environments-v02.zip`, extract it, and serve the directory with `python -m http.server 8000`. The browser can edit a draft and replay saved native motion. Running new physics requires the local Python runtime and verified PiPER assets; a static hosted page does not itself run the native simulator. See `guide.md` for installation, local UI/Terminal commands, and supported inputs.
+
+PiPER holds its native home controls. No grasp policy is trained, no video-to-skill learning or physical-robot transfer is demonstrated, and the consistency audit does not establish real-world physics or sensor accuracy. Negative controls explicitly disable gravity in two stationary verifier fixtures and set initial velocity with zero friction in one moving fixture; all interventions and attempts are retained. Geometry is stored once by content hash.
+
+Original DVIDIA code and geometry: MIT; pinned PiPER simulation assets: MIT with notices in the archive. Other dependencies retain their own licenses. DVIDIA research · [@iammrriver](https://x.com/iammrriver) · hello@dvidia.org.

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audit.json

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environment.json

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+{
+  "machine": "arm64",
+  "mujoco": "3.15.0",
+  "numpy": "2.5.3",
+  "openusd": [
+    0,
+    26,
+    11
+  ],
+  "package_preview": "0.1.3.dev2",
+  "physics_gpu_required": false,
+  "python": "3.12.9",
+  "scope": "Shared-host CPU study; no hardware minimum or hard real-time qualification",
+  "source_freeze_commit": "14759c5",
+  "system": "Darwin",
+  "viewer": {
+    "esbuild": "0.25.10",
+    "three": "0.180.0"
+  }
+}

guide.md

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+# DVIDIA task environments v0.2
+
+Build a small, reproducible practice environment before training a skill. This
+preview adds several object instances, an open bin, explicit local OpenUSD,
+native PiPER physics and a completion check that considers the whole object.
+The local editor and Terminal commands use the same recipe and native runner.
+
+The browser edits a **draft** and replays **recorded native physics**. Simulation
+runs in the local Python process; the browser does not run a physics engine.
+The arm holds its home commands during these environment checks. This release
+does not train a placement policy or infer motor actions from a human video.
+
+## Install the preview
+
+Use Python 3.12 and a fresh virtual environment. Install the wheel from the
+[task environment release](https://github.com/Dvidia-Inference/dvidia-training/releases/tag/task-environments-v0.2-preview)
+with its optional USD/native dependencies:
+
+```sh
+python3.12 -m venv .venv
+source .venv/bin/activate
+python -m pip install './dvidia_training-0.1.3.dev2-py3-none-any.whl[usd]'
+dvidia-robot-sim fetch piper --output robot-assets
+dvidia-environment studio --assets robot-assets --output local-scenes
+```
+
+Open the displayed `http://127.0.0.1:8346/` address. Select an object, drag it in
+the table plane or edit its metric position, then run a preview. Invalid layouts
+are explained before stepping physics. Export/import the recipe to share a
+layout. Run outputs, including incomplete attempts, stay in `local-scenes`.
+Stop the server with Ctrl-C. It binds only to loopback and accepts no remote
+credentials, uploaded Python or robot commands. After dependencies and pinned
+robot assets are installed, these native runs need no network service.
+
+Physics runs on CPU without CUDA. The browser needs WebGL2, JavaScript and
+DecompressionStream; integrated graphics are sufficient for our tested preview.
+The tested host is Apple arm64 with Python 3.12.9, NumPy 2.5.3, MuJoCo 3.15.0 and
+OpenUSD 26.11. Other operating systems and hardware minima are not qualified.
+For planning, allow several GB for the Python environment and verified robot
+assets. Timings are shared-host measurements, not hard real-time guarantees.
+
+## Run the same workflow in Terminal
+
+```sh
+dvidia-environment init --output my-scene
+cp my-scene/recipe.json draft.json
+# Edit draft.json; the original authored scene remains unchanged.
+dvidia-environment validate --recipe draft.json
+dvidia-environment author --recipe draft.json --output edited-scene
+dvidia-environment run --recipe edited-scene/recipe.json --assets robot-assets --output my-run
+dvidia-environment export --study my-run --output my-viewer
+python -m http.server 8000 --directory my-viewer --bind 127.0.0.1
+```
+
+Open `http://127.0.0.1:8000/` to inspect the saved native motion. Output commands
+refuse existing destinations. `workstation.usda` is deterministically authored
+from the recipe and re-read with the real USD SDK before native compilation;
+editing the USD without changing the recipe causes rejection. This is a bounded
+recipe compiler, not an arbitrary USD or NVIDIA asset importer.
+
+## What the recipe means
+
+All distances use metres, masses kilograms, angles radians and quaternions wxyz.
+Z points up, gravity is 9.81 m/s² downward, physics steps every 1 ms, and robot
+control boundaries are 20 ms. Object positions refer to their **bottom centre**;
+the bin position refers to its **outside bottom centre**. Sizes are full XYZ
+dimensions. Contact properties are explicitly labelled `authored_assumption`.
+
+- One pinned six-axis PiPER with its coupled parallel gripper and seven controls.
+- A horizontal static tabletop with its upper surface at Z=0.
+- One open-top bin built from a floor and four separate wall colliders.
+- One to four rigid objects: boxes or an original stepped-box shoe surrogate.
+- Versioned source, catalog and robot asset locks, with exact USD byte identity.
+
+Validation rejects duplicate IDs, nonfinite or unsupported fields, overlapping
+initial object/bin colliders, table overhangs and objects that cannot fit upright
+inside the bin. It allows an explicitly bounded initial drop. It does not prove
+collision-free arm reachability, grasp feasibility or a safe insertion path.
+The bin remains static. Shoe geometry and mass distribution are synthetic;
+deformation, fragile shells and measured material properties remain separate.
+
+## What counts as completed
+
+For **every object**, every corner of every collision piece must fit in the
+rotated bin interior. The object must contact the bin floor, have no robot or
+gripper contact and stay below the recipe's linear/angular speed limits for the
+entire dwell interval. All required objects must pass together. The default
+dwell is 250 ms; bounded contact penetration permits a declared 1 mm floor
+tolerance. Time reversals, observation gaps and invalid native states clear
+history and remain explicitly unknown.
+
+This verifier reads native ground truth for simulation evaluation. It is not a
+camera estimator and is not supplied to a robot as an unmeasured sensor.
+Likewise, a passive object settling in a bin is an environment fixture, not a
+learned robotic placement. Later training can use this contract to evaluate
+executed actions without relying on whether an image merely looks finished.
+
+## Reproduce the frozen checks
+
+```sh
+dvidia-environment benchmark prepare --output scene-study
+dvidia-environment benchmark run --output scene-study --assets robot-assets
+dvidia-environment export --study scene-study --output study-viewer
+```
+
+Preparation fixes source identities, thresholds, case roster and inputs before
+execution. A run writes an intent and attempt evidence; it refuses to repeat an
+already started study. Interrupted attempts remain incomplete, never silently
+retried or counted as successful. The registered study separates accepted
+completion fixtures, negative completion controls and rejected layout inputs.
+Their agreement rate is not a robot skill success rate.
+
+## Toward video-to-skill training
+
+A video can propose object identities, relationships and a destination. To turn
+that into a useful training environment we must also recover metric scale,
+camera/robot frames, contact mechanics and the actual action history. This
+release supplies the bounded scene and independent evaluation portion. The next
+learning experiment needs causal object pose estimates and corrective action
+demonstrations, then held-out start positions and perturbations. It should beat
+the fixed controller before becoming an installable skill.
+
+Original DVIDIA code, authored scenes and synthetic rigid assets: MIT. Pinned
+PiPER assets retain their MIT notices; Three.js and other dependencies retain
+their licenses. No new learned model weights are published with this study.
+DVIDIA research · [@iammrriver](https://x.com/iammrriver) · hello@dvidia.org.

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