Pepijn
1157fb11e6
- envs(robotwin): default `observation_height/width` in `create_robotwin_envs` to `DEFAULT_CAMERA_H/W` (240/320) so they match the D435 dims baked into `task_config/demo_clean.yml`. - envs(robotwin): resolve `task_config/demo_clean.yml` via `CONFIGS_PATH` instead of a cwd-relative path; works regardless of where `lerobot-eval` is invoked. - envs(robotwin): replace `print()` calls in `create_robotwin_envs` with `logger.info(...)` (module-level `logger = logging.getLogger`). - envs(robotwin): use `_LazyAsyncVectorEnv` for the async path so async workers start lazily (matches LIBERO / RoboCasa / VLABench). - envs(robotwin): cast `agent_pos` space + joint-state output to float32 end-to-end (was mixed float64/float32). - envs(configs): use the existing `_make_vec_env_cls(use_async, n_envs)` helper in `RoboTwinEnvConfig.create_envs`; drop the `get_env_processors` override so RoboTwin uses the identity processor inherited from `EnvConfig`. - processor: delete `RoboTwinProcessorStep` — the float32 cast now happens in the wrapper itself, so the processor is redundant. - tests: drop the `TestRoboTwinProcessorStep` suite; update the mock obs fixture to use float32 `joint_action.vector`. - ci: hoist `ROBOTWIN_POLICY` and `ROBOTWIN_TASKS` to job-level env vars so the task list and policy aren't duplicated across eval / extract / parse steps. - docker: pin RoboTwin + CuRobo upstream clones to commit SHAs (`RoboTwin@0aeea2d6`, `curobo@ca941586`) for reproducibility. Co-Authored-By: Claude Opus 4.7 (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