CONTRIBUTING.md

How to contribute to 🤗 LeRobot?

Everyone is welcome to contribute, and we value everybody's contribution. Code is thus not the only way to help the community. Answering questions, helping others, reaching out and improving the documentations are immensely valuable to the community.

It also helps us if you spread the word: reference the library from blog posts on the awesome projects it made possible, shout out on Twitter when it has helped you, or simply ⭐️ the repo to say "thank you".

Whichever way you choose to contribute, please be mindful to respect our code of conduct.

You can contribute in so many ways!

Some of the ways you can contribute to 🤗 LeRobot:

• Fixing outstanding issues with the existing code.
• Implementing new models, datasets or simulation environments.
• Contributing to the examples or to the documentation.
• Submitting issues related to bugs or desired new features.

Following the guides below, feel free to open issues and PRs and to coordinate your efforts with the community on our Discord Channel. For specific inquiries, reach out to Remi Cadene.

If you are not sure how to contribute or want to know the next features we working on, look on this project page: LeRobot TODO

Submitting a new issue or feature request

Do your best to follow these guidelines when submitting an issue or a feature request. It will make it easier for us to come back to you quickly and with good feedback.

Did you find a bug?

The 🤗 LeRobot library is robust and reliable thanks to the users who notify us of the problems they encounter. So thank you for reporting an issue.

First, we would really appreciate it if you could make sure the bug was not already reported (use the search bar on Github under Issues).

Did not find it? :( So we can act quickly on it, please follow these steps:

• Include your OS type and version, the versions of Python and PyTorch.
• A short, self-contained, code snippet that allows us to reproduce the bug in less than 30s.
• The full traceback if an exception is raised.
• Attach any other additional information, like screenshots, you think may help.

Do you want a new feature?

A good feature request addresses the following points:

1. Motivation first:

• Is it related to a problem/frustration with the library? If so, please explain why. Providing a code snippet that demonstrates the problem is best.
• Is it related to something you would need for a project? We'd love to hear about it!
• Is it something you worked on and think could benefit the community? Awesome! Tell us what problem it solved for you.

2. Write a paragraph describing the feature.
3. Provide a code snippet that demonstrates its future use.
4. In case this is related to a paper, please attach a link.
5. Attach any additional information (drawings, screenshots, etc.) you think may help.

If your issue is well written we're already 80% of the way there by the time you post it.

Adding new policies, datasets or environments

Look at our implementations for datasets, policies, environments (aloha, xarm, pusht) and follow the same api design.

When implementing a new dataset loadable with LeRobotDataset follow these steps:

• Update available_datasets_per_env in lerobot/__init__.py

When implementing a new environment (e.g. gym_aloha), follow these steps:

• Update available_tasks_per_env and available_datasets_per_env in lerobot/__init__.py

When implementing a new policy class (e.g. DiffusionPolicy) follow these steps:

• Update available_policies and available_policies_per_env, in lerobot/__init__.py
• Set the required name class attribute.
• Update variables in tests/test_available.py by importing your new Policy class

Submitting a pull request (PR)

Before writing code, we strongly advise you to search through the existing PRs or issues to make sure that nobody is already working on the same thing. If you are unsure, it is always a good idea to open an issue to get some feedback.

You will need basic git proficiency to be able to contribute to 🤗 LeRobot. git is not the easiest tool to use but it has the greatest manual. Type git --help in a shell and enjoy. If you prefer books, Pro Git is a very good reference.

Follow these steps to start contributing:

1. Fork the repository by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account.

2. Clone your fork to your local disk, and add the base repository as a remote. The following command assumes you have your public SSH key uploaded to GitHub. See the following guide for more information.

   git clone git@github.com:<your Github handle>/lerobot.git
   cd lerobot
   git remote add upstream https://github.com/huggingface/lerobot.git

3. Create a new branch to hold your development changes, and do this for every new PR you work on.

   Start by synchronizing your main branch with the upstream/main branch (more details in the GitHub Docs):

   git checkout main
   git fetch upstream
   git rebase upstream/main

   Once your main branch is synchronized, create a new branch from it:

   git checkout -b a-descriptive-name-for-my-changes

   🚨 Do not work on the main branch.

4. for development, we use poetry instead of just pip to easily track our dependencies. If you don't have it already, follow the instructions to install it.

   Set up a development environment with conda or miniconda:

   conda create -y -n lerobot-dev python=3.10 && conda activate lerobot-dev

   To develop on 🤗 LeRobot, you will at least need to install the dev and test extras dependencies along with the core library:

   poetry install --sync --extras "dev test"

   You can also install the project with all its dependencies (including environments):

   poetry install --sync --all-extras

   Note: If you don't install simulation environments with --all-extras, the tests that require them will be skipped when running the pytest suite locally. However, they will be tested in the CI. In general, we advise you to install everything and test locally before pushing.

   Whichever command you chose to install the project (e.g. poetry install --sync --all-extras), you should run it again when pulling code with an updated version of pyproject.toml and poetry.lock in order to synchronize your virtual environment with the new dependencies.

   The equivalent of pip install some-package, would just be:

   poetry add some-package

   When making changes to the poetry sections of the pyproject.toml, you should run the following command to lock dependencies.

   poetry lock --no-update

5. Develop the features on your branch.

   As you work on the features, you should make sure that the test suite passes. You should run the tests impacted by your changes like this (see below an explanation regarding the environment variable):

   pytest tests/<TEST_TO_RUN>.py

6. Follow our style.

   lerobot relies on ruff to format its source code consistently. Set up pre-commit to run these checks automatically as Git commit hooks.

   Install pre-commit hooks:

   pre-commit install

   You can run these hooks whenever you need on staged files with:

   pre-commit

   Once you're happy with your changes, add changed files using git add and make a commit with git commit to record your changes locally:

   git add modified_file.py
   git commit

   Note, if you already commited some changes that have a wrong formatting, you can use:

   pre-commit run --all-files

   Please write good commit messages.

   It is a good idea to sync your copy of the code with the original repository regularly. This way you can quickly account for changes:

   git fetch upstream
   git rebase upstream/main

   Push the changes to your account using:

   git push -u origin a-descriptive-name-for-my-changes

7. Once you are satisfied (and the checklist below is happy too), go to the webpage of your fork on GitHub. Click on 'Pull request' to send your changes to the project maintainers for review.

8. It's ok if maintainers ask you for changes. It happens to core contributors too! So everyone can see the changes in the Pull request, work in your local branch and push the changes to your fork. They will automatically appear in the pull request.

Checklist

1. The title of your pull request should be a summary of its contribution;
2. If your pull request addresses an issue, please mention the issue number in the pull request description to make sure they are linked (and people consulting the issue know you are working on it);
3. To indicate a work in progress please prefix the title with [WIP], or preferably mark the PR as a draft PR. These are useful to avoid duplicated work, and to differentiate it from PRs ready to be merged;
4. Make sure existing tests pass;

Tests

An extensive test suite is included to test the library behavior and several examples. Library tests can be found in the tests folder.

Install git lfs to retrieve test artifacts (if you don't have it already).

On Mac:

brew install git-lfs
git lfs install

On Ubuntu:

sudo apt-get install git-lfs
git lfs install

Pull artifacts if they're not in tests/data

git lfs pull

We use pytest in order to run the tests. From the root of the repository, here's how to run tests with pytest for the library:

DATA_DIR="tests/data" python -m pytest -sv ./tests

You can specify a smaller set of tests in order to test only the feature you're working on.