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chore(docs): update readme + gr00t libero results (#3941)

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* chore(docs): update readme + gr00t libero results

* chore(docs): update template and in-tree policy steps
This commit is contained in:
Steven Palma
2026-07-05 15:11:46 +02:00
committed by GitHub
parent 192a0b9282
commit 7957d4e2dc
4 changed files with 30 additions and 18 deletions
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docs/source/bring_your_own_policies.mdx
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@@ -295,11 +295,12 @@ The file names are load-bearing: the factory does lazy imports by name, and the
### Wiring
Three places need to know about your policy. All by name.
Four places need to know about your policy. All by name.
1. **`policies/__init__.py`** — re-export `MyPolicyConfig` and add it to `__all__`. **Don't** re-export the modeling class; it loads lazily through the factory (so `import lerobot` stays fast).
2. **`factory.py:get_policy_class`** — add a branch returning `MyPolicy` from a lazy import.
3. **`factory.py:make_policy_config`** and **`factory.py:make_pre_post_processors`** — same idea, two more branches.
4. **`templates/lerobot_modelcard_template.md` and the root `README.md`** — the template is what `push_model_to_hub` renders into the model card of every checkpoint trained with your policy: add a one-line description of your policy in the `model_name` branches, map it in `policy_docs` so cards link to your MDX guide, and optionally add an architecture image to `diagrams`. Then add your policy to the models table in the root `README.md`, under the right category, linking to your doc page.
Mirror an existing policy that's structurally similar to yours; the diff is small.
@@ -371,6 +372,8 @@ The general expectations are in [`CONTRIBUTING.md`](https://github.com/huggingfa
- [ ] Optional deps live behind a `[project.optional-dependencies]` extra and the `TYPE_CHECKING + require_package` guard.
- [ ] `tests/policies/` updated; backward-compat artifact committed & policy-specific tests.
- [ ] `src/lerobot/policies/<name>/README.md` symlinked into `docs/source/policy_<name>_README.md`; user-facing `docs/source/<name>.mdx` written and added to `_toctree.yml`.
- [ ] `templates/lerobot_modelcard_template.md` has a description entry and a `policy_docs` link for your policy.
- [ ] The models table in the root `README.md` lists your policy in the right category, linking to your doc page.
- [ ] At least one reproducible benchmark eval in the policy MDX with a published checkpoint (sim benchmark, or real-robot dataset + checkpoint).
The fastest way to get a clean PR is to copy the directory of the existing policy closest to yours, rename, and replace contents method by method. Don't wait until everything is polished — open a draft PR early and iterate with us; reviewers would much rather give feedback on a half-finished branch than a fully-merged one.
docs/source/groot.mdx
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@@ -160,13 +160,13 @@ This will follow the recipe found [here](https://github.com/NVIDIA/Isaac-GR00T/b
Preliminary LeRobot integration results (GR00T-LeRobot, `eval.n_episodes >= 50` per suite):
| Suite | Success rate |
| ---------------- | -----------: |
| LIBERO Spatial | 94% |
| LIBERO Object | 98% |
| LIBERO Goal | 93% |
| LIBERO 10 (Long) | 90% |
| **Average** | **93.75%** |
| Suite | Success rate | Checkpoint |
| ---------------- | -----------: | ------------------------------------------------------------------------------------------------------------- |
| LIBERO Spatial | 91% | [nvidia/gr00t17-lerobot-libero_spatial-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_spatial-640) |
| LIBERO Object | 81% | [nvidia/gr00t17-lerobot-libero_object-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_object-640) |
| LIBERO Goal | 97% | [nvidia/gr00t17-lerobot-libero_goal-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_goal-640) |
| LIBERO 10 (Long) | 84% | [nvidia/gr00t17-lerobot-libero_10-640](https://huggingface.co/nvidia/gr00t17-lerobot-libero_10-640) |
| **Average** | **88.25%** | |
```bash
export MODEL_ID=your_trained_model_on_huggingface