Pepijn
69ed70edeb
Add RoboCasa365 (arXiv:2603.04356) as a new simulation benchmark with 365 everyday kitchen manipulation tasks across 2,500 diverse environments. New files: - src/lerobot/envs/robocasa.py: gym.Env wrapper with deferred env creation, flat 12D action / 16D state vectors, 3-camera support - docs/source/robocasa.mdx: user-facing documentation - docker/Dockerfile.benchmark.robocasa: CI benchmark image Modified files: - src/lerobot/envs/configs.py: RoboCasaEnv config (--env.type=robocasa) - pyproject.toml: robocasa optional dependency group - docs/source/_toctree.yml: sidebar entry - .github/workflows/benchmark_tests.yml: integration test job Refs: https://arxiv.org/abs/2603.04356, https://robocasa.ai Related: huggingface/lerobot#321 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Docker
This directory contains Dockerfiles for running LeRobot in containerized environments. Both images are built nightly from main and published to Docker Hub with the full environment pre-baked — no dependency setup required.
Pre-built Images
# CPU-only image (based on Dockerfile.user)
docker pull huggingface/lerobot-cpu:latest
# GPU image with CUDA support (based on Dockerfile.internal)
docker pull huggingface/lerobot-gpu:latest
Quick Start
The fastest way to start training is to pull the GPU image and run lerobot-train directly. This is the same environment used for all of our CI, so it is a well-tested, batteries-included setup.
docker run -it --rm --gpus all --shm-size 16gb huggingface/lerobot-gpu:latest
# inside the container:
lerobot-train --policy.type=act --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human
Dockerfiles
Dockerfile.user (CPU)
A lightweight image based on python:3.12-slim. Includes all Python dependencies and system libraries but does not include CUDA — there is no GPU support. Useful for exploring the codebase, running scripts, or working with robots, but not practical for training.
Dockerfile.internal (GPU)
A CUDA-enabled image based on nvidia/cuda. This is the image for training — mostly used for internal interactions with the GPU cluster.
Usage
Running a pre-built image
# CPU
docker run -it --rm huggingface/lerobot-cpu:latest
# GPU
docker run -it --rm --gpus all --shm-size 16gb huggingface/lerobot-gpu:latest
Building locally
From the repo root:
# CPU
docker build -f docker/Dockerfile.user -t lerobot-user .
docker run -it --rm lerobot-user
# GPU
docker build -f docker/Dockerfile.internal -t lerobot-internal .
docker run -it --rm --gpus all --shm-size 16gb lerobot-internal
Multi-GPU training
To select specific GPUs, set CUDA_VISIBLE_DEVICES when launching the container:
# Use 4 GPUs
docker run -it --rm --gpus all --shm-size 16gb \
-e CUDA_VISIBLE_DEVICES=0,1,2,3 \
huggingface/lerobot-gpu:latest
USB device access (e.g. robots, cameras)
docker run -it --device=/dev/ -v /dev/:/dev/ --rm huggingface/lerobot-cpu:latest