Files
lerobot-clone/docs
Pepijn 282c31cfef feat(envs): add RoboMME benchmark (#3311)
* feat(envs): add RoboMME benchmark integration

- RoboMME env wrapper with image/wrist_image/state observations
- Docker image with Vulkan, SAPIEN, mani-skill deps
- CI workflow: 1-episode smoke eval with pepijn223/smolvla_robomme
- preprocess_observation: handle image/wrist_image/state keys
- pyproject.toml: robomme extra

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* refactor(docker): rebase RoboMME image on huggingface/lerobot-gpu

Mirror the libero/metaworld pattern: start from the nightly GPU image
(which already has apt deps, uv, venv, and lerobot[all] preinstalled)
and only layer on what RoboMME uniquely needs — the Vulkan libs
ManiSkill/SAPIEN requires, plus the robomme extra with the
gymnasium/numpy overrides.

Drops 48 lines of duplicated base setup (CUDA FROM, python install,
user creation, venv init, base apt deps) that the nightly image already
provides. Net: 102 → 54 lines.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* docs(robomme): drop prototype-branch note and move dataset to lerobot/robomme

- Remove the "Related work" block referencing the prototype branch
  feat/robomme-integration; the PR stands on its own.
- Point all dataset references at lerobot/robomme (docs, env module
  docstring, RoboMMEEnvConfig docstring) — this is the canonical HF
  location once the dataset is mirrored.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(robomme): make docs build + fast tests green

1. Docs: add robomme to _toctree.yml under Benchmarks so doc-builder's
   TOC integrity check stops rejecting the new page.

2. Fast tests: robomme's mani-skill transitively pins numpy<2 which is
   unsatisfiable against the project's numpy>=2 base pin, so `uv sync`
   couldn't resolve a universal lockfile.

   Drop robomme as a pyproject extra entirely — it truly cannot coexist
   with the rest of the dep tree. The Dockerfile installs robomme
   directly from its git URL via `uv pip install --override`, which was
   already the runtime path. pyproject, docs, env docstrings, and the
   CI job comment all now point to the docker-only install.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* test(robomme): realign unit tests with current env API

The tests were written against an earlier env layout and never updated when
the wrapper was refactored, so CI's fast-test job was failing with:

- KeyError: 'front_rgb' / 'wrist_rgb' — these were renamed to the
  lerobot-canonical 'image' / 'wrist_image' keys (matching the dataset
  columns and preprocess_observation's built-in fallbacks).
- AssertionError: 'robomme' not in result — create_robomme_envs now
  returns {task_name: {task_id: env}}, not {'robomme': {...}}, so
  comma-separated task lists work.
- ModuleNotFoundError: lerobot.envs.lazy_vec_env — LazyVectorEnv was
  removed; create_robomme_envs is straightforward synchronous now.

Rewrite the 7 failing cases against the current API, drop the three
LazyVectorEnv tests, and add a multi-task test so the new comma-separated
task parsing is covered. Stub install/teardown is moved into helpers
(`_install_robomme_stub` / `_uninstall_robomme_stub`) so individual tests
stop repeating six boilerplate lines.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* ci: point benchmark eval checkpoints at the lerobot/ org mirrors

pepijn223/smolvla_* → lerobot/smolvla_* across every benchmark job in
this branch (libero, metaworld, and the per-branch benchmark). The
checkpoints were mirrored into the lerobot/ org and that's the canonical
location going forward.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: integrate PR #3311 review feedback

- envs: rename obs keys to pixels/image, pixels/wrist_image, agent_pos
- envs: add __post_init__ for dynamic action_dim in RoboMMEEnv config
- envs: remove special-case obs conversion in utils.py (no longer needed)
- ci: add Docker Hub login, HF_USER_TOKEN guard, --env.task_ids=[0]
- scripts: extract_task_descriptions supports multiple task_ids
- docs: title to # RoboMME, add image, restructure eval section
- tests: update all key assertions to match new obs naming

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix(docs): use correct RoboMME teaser image URL

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* ci(robomme): smoke-eval 10 tasks instead of 5

Broader coverage on the RoboMME benchmark CI job: bump the smoke eval
from 5 tasks to 10 (one episode each), all drawn from ROBOMME_TASKS.

Tasks now run: PickXtimes, BinFill, StopCube, MoveCube, InsertPeg,
SwingXtimes, VideoUnmask, ButtonUnmask, PickHighlight, PatternLock.

Updated the parse_eval_metrics.py `--task` label from the single
`PickXtimes` stub to the full comma list so the metrics artifact
reflects what was actually run. `parse_eval_metrics.py` already reads
`overall` for multi-task runs, so no parser change is needed.

Made-with: Cursor

* fix(robomme): nest `pixels` as a dict so preprocess_observation picks it up

`_convert_obs` was returning flat keys (`pixels/image`,
`pixels/wrist_image`). `preprocess_observation()` in envs/utils.py
keys off the top-level `"pixels"` entry and, not finding it,
silently dropped every image from the batch. The policy then saw
zero image features and raised

    ValueError: All image features are missing from the batch.

Match the LIBERO layout: return
`{"pixels": {"image": ..., "wrist_image": ...}, "agent_pos": ...}`
and declare the same shape in `observation_space`.

Made-with: Cursor

* fix(robomme): align docs and tests with nested pixels obs layout

Addresses PR #3311 review feedback:
- Docs: correct observation keys to `pixels/image` / `pixels/wrist_image`
  (mapped to `observation.images.image` / `observation.images.wrist_image`)
  and drop the now-obsolete column-rename snippet.
- Tests: assert `result["pixels"]["image"]` instead of flat `pixels/image`,
  matching the nested layout required by `preprocess_observation()`.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(envs): preserve AsyncVectorEnv metadata/unwrapped in lazy eval envs

Port of #3416 onto this branch.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* ci: gate Docker Hub login on secret availability

Fork PRs cannot access `secrets.DOCKERHUB_LEROBOT_{USERNAME,PASSWORD}`,
which made every benchmark job fail at the login step. Gate the login
on the env-var expansion of the username so the step is skipped (not
failed) when secrets are absent.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

* fix(robomme): address review feedback

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-20 20:21:27 +02:00
..

Generating the documentation

To generate the documentation, you first have to build it. Several packages are necessary to build the doc, you can install them with the following command, at the root of the code repository:

pip install -e . -r docs-requirements.txt

You will also need nodejs. Please refer to their installation page


NOTE

You only need to generate the documentation to inspect it locally (if you're planning changes and want to check how they look before committing for instance). You don't have to git commit the built documentation.


Building the documentation

Once you have setup the doc-builder and additional packages, you can generate the documentation by typing the following command:

doc-builder build lerobot docs/source/ --build_dir ~/tmp/test-build

You can adapt the --build_dir to set any temporary folder that you prefer. This command will create it and generate the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite Markdown editor.

Previewing the documentation

To preview the docs, first install the watchdog module with:

pip install watchdog

Then run the following command:

doc-builder preview lerobot docs/source/

The docs will be viewable at http://localhost:3000. You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.


NOTE

The preview command only works with existing doc files. When you add a completely new file, you need to update _toctree.yml & restart preview command (ctrl-c to stop it & call doc-builder preview ... again).


Adding a new element to the navigation bar

Accepted files are Markdown (.md).

Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting the filename without the extension in the _toctree.yml file.

Renaming section headers and moving sections

It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.

Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.

So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:

Sections that were moved:

[ <a href="#section-b">Section A</a><a id="section-a"></a> ]

and of course, if you moved it to another file, then:

Sections that were moved:

[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]

Use the relative style to link to the new file so that the versioned docs continue to work.

For an example of a rich moved sections set please see the very end of the transformers Trainer doc.

Adding a new tutorial

Adding a new tutorial or section is done in two steps:

  • Add a new file under ./source. This file can either be ReStructuredText (.rst) or Markdown (.md).
  • Link that file in ./source/_toctree.yml on the correct toc-tree.

Make sure to put your new file under the proper section. If you have a doubt, feel free to ask in a Github Issue or PR.

Writing source documentation

Values that should be put in code should either be surrounded by backticks: `like so`. Note that argument names and objects like True, None or any strings should usually be put in code.

Writing a multi-line code block

Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:

```
# first line of code
# second line
# etc
```

Adding an image

Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted dataset like the ones hosted on hf-internal-testing in which to place these files and reference them by URL. We recommend putting them in the following dataset: huggingface/documentation-images. If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images to this dataset.