Pepijn
1ff10b935c
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.ymlon 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.