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Co-authored-by: Haoming Song <1847575517@qq.com>
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
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Steven Palma
2026-05-08 12:50:34 +02:00
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parent ecd38c50d7
commit 3d5bc8bdf1
1 changed files with 5 additions and 5 deletions
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docs/source/contributing_a_policy.mdx
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@@ -12,7 +12,7 @@ A note on tone: robot-learning is an actively evolving field, and "what a policy
```
src/lerobot/policies/my_policy/
├── __init__.py # re-exports config + processor factory (NOT modeling)
├── __init__.py # re-exports config + modeling + processor factory
├── configuration_my_policy.py # MyPolicyConfig + @register_subclass
├── modeling_my_policy.py # MyPolicy(PreTrainedPolicy)
├── processor_my_policy.py # make_my_policy_pre_post_processors
@@ -46,7 +46,7 @@ The methods called by the train/eval loops:
| `select_action(batch, **kwargs) -> Tensor` | `lerobot-eval` | Return the next action `(B, action_dim)`. Called every step. |
| `predict_action_chunk(batch, **kwargs) -> Tensor` | the policy itself | Return an action chunk `(B, chunk_size, action_dim)`. Currently abstract on the base class — raise `NotImplementedError` if your policy doesn't chunk. |
| `forward(batch, reduction="mean") -> tuple[Tensor, dict \| None]` | `lerobot-train` | Return `(loss, output_dict)`. Must accept `reduction="none"` for per-sample weighting. |
| `get_optim_params() -> dict` | the optimizer | Return parameter groups; `{"params": self.parameters()}` is fine if you don't need per-group settings. |
| `get_optim_params() -> dict` | the optimizer | Return `self.parameters()` for simple policies; return a named parameter dict for [multi-optimizer policies](https://github.com/huggingface/lerobot/blob/ecd38c50d7d15b4184cf42649ff1185ee2e11eeb/src/lerobot/policies/sac/modeling_sac.py#L61-L73). |
| `update() -> None` _(optional)_ | `lerobot-train` | Called after each optimizer step _if defined_. Use for EMA, target nets, replay buffers (TDMPC uses this). |
Batches are flat dictionaries keyed by the constants in [`lerobot.utils.constants`](https://github.com/huggingface/lerobot/blob/main/src/lerobot/utils/constants.py): `OBS_STATE` (`observation.state.<motor>`), `OBS_IMAGES` (`observation.images.<camera>`), `OBS_LANGUAGE`, `ACTION`, etc. Reuse the constants — don't invent new prefixes.
@@ -112,7 +112,7 @@ A new policy is much easier to review — and far more useful — when it ships
**Pick at least one in-tree benchmark.** LeRobot ships sim benchmarks with per-benchmark Docker images (LIBERO, LIBERO-plus, Meta-World, RoboTwin 2.0, RoboCasa365, RoboCerebra, RoboMME, VLABench and more). Pick the one that matches your policy's modality — VLAs usually go to LIBERO or VLABench; image-only BC to LIBERO or Meta-World. The full list lives under [Benchmarks](./libero) in the docs sidebar.
**Push the checkpoint** to the Hub under `lerobot/<policy>_<benchmark>` (or your namespace if you don't have write access; a maintainer can mirror it). Use `PreTrainedPolicy.push_to_hub` so the repo gets `config.json`, `model.safetensors`, and a model card.
**Push the checkpoint** to the Hub under `lerobot/<policy>_<benchmark>` (or your namespace if you don't have write access; a maintainer can mirror it). Use `PreTrainedPolicy.push_model_to_hub` so the repo gets `config.json`, `model.safetensors`, and a model card.
**Report results in your policy's MDX**, with the exact `lerobot-eval` command and hardware so anyone can re-run:
@@ -134,7 +134,7 @@ Reproduce: `lerobot-eval --policy.path=lerobot/<policy>_libero --env.type=libero
Use `n_episodes ≥ 50` per suite for stable success-rate estimates.
If your policy is real-robot-only and no sim benchmark applies, swap the sim eval for: a public training dataset on the Hub, the `lerobot-train` command, the checkpoint, and a real-robot success rate over ≥10 episodes via `lerobot-record --policy.path=...`.
If your policy is real-robot-only and no sim benchmark applies, swap the sim eval for: a public training dataset on the Hub, the `lerobot-train` command, the checkpoint, and a real-robot success rate over ≥10 episodes via `lerobot-rollout --policy.path=...`.
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