| # 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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