# RoboDojo environment

This directory owns the native Direct v2 worker, robot-only kinematics and
hash-bound runtime preparation. It contains no model policy, prompt, provider
client, training recipe or model result.

Task definitions and fixed panels live in [../bench_server/protocol/task_catalog.json](../bench_server/protocol/task_catalog.json).
The [service adapter](../bench_server/adapter.py) uses
[native_worker_client.py](../bench_server/native_worker_client.py) to communicate
with this directory's worker. That private connection is separate from the public
SIM service used by model runners. Deployment paths and GPU selection are supplied
through [../server.example.json](../server.example.json).

## Required runtime

For a fresh deployment from official public sources without the historical
private parent manifest, use [official-source deployment v1](OFFICIAL_RUNTIME.md)
and set `deployment_track: "official"` in the existing server configuration.
The historical deployment described below remains available unchanged.

Use legally obtained RoboDojo source/assets and the exact revisions recorded in
[reference_eef_snapshot.json](reference_eef_snapshot.json). The accepted
deployment baseline uses Python 3.11, Isaac Sim 5.1, IsaacLab 0.54.3, Torch
2.7.0+cu128, NumPy 1.26.0, SciPy 1.15.3, Gymnasium 1.2.1 and PyAV 18.1.0.
These are validation pins, not an automatic installer.

The operator must provide a compatible NVIDIA driver/GPU and accept applicable
runtime and asset licences. The tools do not copy an EULA marker, credentials or
licensed assets. Do not fabricate a manifest, weaken source checks or silently
replace unresolved materials.

## Prepare an owned deployment

The create-only preparation tool copies verified source and assets into a new
runtime root and emits an immutable manifest/digest:

```sh
python -B -m benchmarks.simBenchmarks.RoboDojo.environment.reference_eef_runtime prepare \
  --native-repository /absolute/upstream/git/repository \
  --base-assets /absolute/qualified-parent/Assets \
  --additional-assets /absolute/authorized-reference/Assets \
  --reference-assets /absolute/authorized-reference/Assets \
  --asset-parent-manifest /absolute/qualified-parent-manifest.json \
  --runtime-root /absolute/new-owned-runtime \
  --python /absolute/native-environment/bin/python \
  --isaaclab-root /absolute/pinned/IsaacLab \
  --curobo-root /absolute/pinned/curobo \
  --recording-support /absolute/recording-support \
  --archive /absolute/new-owned-runtime.tar.gz
```

Asset locations are operator-supplied paths. `--base-assets` supplies the exact
parent `files` inventory; `--additional-assets` supplies the pinned additional
asset leaves. The optional `--reference-assets` independently rechecks the
parent manifest's `reference_files` at a current location, including its absent
counterparts and declared URDF line-ending equivalence. It may point to the same
tree as `--additional-assets`, but the two options verify different inventories.
When omitted, preparation still verifies every base and additional asset byte;
it does not access the historical counterpart directory. The preparation receipt
records whether the optional counterpart comparison ran and its resolved path.

The parent manifest's `reference_root` is historical provenance, not a hardcoded
deployment requirement. Keep the original parent manifest bytes and pinned
digest when relocating assets. The service reads the prepared runtime's `Assets`
tree via its configured runtime manifest; it does not need the original
reference directory. No asset-root field is passed to the model.

`KNOWN_UNRESOLVED` records exact inherited material/texture references inside
the pinned assets. Those URI strings identify accepted missing references;
they are not deployment paths or configurable asset roots.

Retain the returned manifest, digest, preparation receipt and archive hash.
External interpreter/dependency paths are part of the deployment binding. A
relocated installation needs a new verified manifest; never edit a manifest
already associated with an episode.

## Readiness gate

Before model evaluation verify:

1. source, asset, layout and dependency hashes;
2. Isaac licence acceptance and actual GPU availability;
3. native reset and all three RGB streams;
4. measured robot state, fixed DLS conversion and one acknowledged control;
5. native scorer accounting, video decode and acknowledged worker cleanup.

A reset-only or hold diagnostic is infrastructure evidence, not task success or
a model score. Store diagnostics outside public capability results. Never add
simulator ticks while waiting for a model: waiting must not change physics.

See [the full Direct v2 binding](REFERENCE_EEF.md) and the parent
[evaluation protocol](../EVALUATION_PROTOCOL.md).