Le Robot

State-of-the-art machine learning for real-world robotics

Le Robot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier for entry to robotics so that everyone can contribute and benefit from sharing datasets and pretrained models.

Le Robot contains state-of-the-art approaches that have been shown to transfer to the real-world with a focus on imitation learning and reinforcement learning.

Le Robot already provides a set of pretrained models, datasets with human collected demonstrations, and simulated environments so that everyone can get started. In the coming weeks, the plan is to add more and more supports for real-world robotics on the most affordable and capable robots out there.

Le Robot is built upon TorchRL which provides abstractions and utilities for Reinforcement Learning.

Acknowledgment

• Our ACT policy and ALOHA environment are adapted from ALOHA
• Our Diffusion policy and Pusht environment are adapted from Diffusion Policy
• Our TDMPC policy and Simxarm environment are adapted from FOWM

Installation

Create a virtual environment with Python 3.10, e.g. using conda:

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

Install poetry (if you don't have it already)

curl -sSL https://install.python-poetry.org | python -

Install dependencies

poetry install

If you encounter a disk space error, try to change your tmp dir to a location where you have enough disk space, e.g.

mkdir ~/tmp
export TMPDIR='~/tmp'

To use Weights and Biases for experiments tracking, log in with

wandb login

Usage

Train

python lerobot/scripts/train.py \
hydra.job.name=pusht \
env=pusht

Visualize offline buffer

python lerobot/scripts/visualize_dataset.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht

Visualize online buffer / Eval

python lerobot/scripts/eval.py \
hydra.run.dir=tmp/$(date +"%Y_%m_%d") \
env=pusht

TODO

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

Ask Remi Cadene for access if needed.

Profile

Example

from torch.profiler import profile, record_function, ProfilerActivity

def trace_handler(prof):
    prof.export_chrome_trace(f"tmp/trace_schedule_{prof.step_num}.json")

with profile(
    activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
    schedule=torch.profiler.schedule(
        wait=2,
        warmup=2,
        active=3,
    ),
    on_trace_ready=trace_handler
) as prof:
    with record_function("eval_policy"):
        for i in range(num_episodes):
            prof.step()

python lerobot/scripts/eval.py \
pretrained_model_path=/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt \
eval_episodes=7

Contribute

Style

# install if needed
pre-commit install
# apply style and linter checks before git commit
pre-commit run -a

Adding dependencies (temporary)

Right now, for the CI to work, whenever a new dependency is added it needs to be also added to the cpu env, eg:

# Run in this directory, adds the package to the main env with cuda
poetry add some-package

# Adds the same package to the cpu env
cd .github/poetry/cpu && poetry add some-package

Tests

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

When adding a new dataset, mock it with

python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET

Run tests

DATA_DIR="tests/data" pytest -sx tests

Datasets

To add a dataset to the hub, first login and use a token generated from huggingface settings with write access:

huggingface-cli login --token ${HUGGINGFACE_TOKEN} --add-to-git-credential

Then you can upload it to the hub with:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset  \
--revision v1.0

You will need to set the corresponding version as a default argument in your dataset class:

  version: str | None = "v1.0",

See: lerobot/common/datasets/pusht.py

For instance, for cadene/pusht, we used:

HF_USER=cadene
DATASET=pusht

If you want to improve an existing dataset, you can download it locally with:

mkdir -p data/$DATASET
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download ${HF_USER}/$DATASET \
--repo-type dataset \
--local-dir data/$DATASET \
--local-dir-use-symlinks=False \
--revision v1.0

Iterate on your code and dataset with:

DATA_DIR=data python train.py

Upload a new version (v2.0 or v1.1 if the changes are respectively more or less significant):

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli upload $HF_USER/$DATASET data/$DATASET \
--repo-type dataset \
--revision v1.1 \
--delete "*"

Then you will need to set the corresponding version as a default argument in your dataset class:

  version: str | None = "v1.1",

See: lerobot/common/datasets/pusht.py

Finally, you might want to mock the dataset if you need to update the unit tests as well:

python tests/scripts/mock_dataset.py --in-data-dir data/$DATASET --out-data-dir tests/data/$DATASET