lerobot

library0.5.1pypypiunverified

LeRobot is a state-of-the-art open-source Python library for real-world robotics using PyTorch, developed by Hugging Face. It provides tools for creating robot environments, loading pre-trained policies, and managing robotics datasets. It is currently at version 0.5.1 and is under active development, with new releases often including significant updates and breaking changes.

ai-ml data

pip install lerobot

github ↗package ↗homepage ↗docs ↗

INSTALL

IMPORT

SIG · LEROBOT

lerobot

ai-mlpythonv0.5.1

Install

—

Import

—

Disk

—

Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

build_error

glibc

py 3.10–3.920 runs

timeout

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

load_policy

✓ from lerobot import load_policy

✗ from lerobot import load_policy

This quickstart demonstrates how to load a pre-trained robotics policy from the Hugging Face Hub and use it to sample actions in a dummy environment. It showcases the core `load_policy` and `RobotEnvFactory` components.

from lerobot.common.policies.hf_hub.load import load_policy from lerobot.common.robot_env_factory import RobotEnvFactory import torch # Load a pre-trained policy from the Hugging Face Hub # This example uses a public model, so no explicit token is needed unless fine-tuning. policy = load_policy("lerobot/diffusion_policy_v2_pen_push_multi_view_taco_play_sok_fixed_camera_v0") # Create a dummy robot environment for demonstration # Real environments require specific configurations like 'robomimic', 'taco_play', etc. # e.g., env = RobotEnvFactory("taco_play", record_dir="./recordings")() env = RobotEnvFactory("dummy")() # Reset the environment to get initial observation obs = env.reset() # Sample actions from the policy with torch.no_grad(): action = policy.sample_actions(obs) print(f"Initial observation keys: {obs.keys()}") print(f"Sampled action shape: {action.shape}") env.close()

Debug

Known issues

breakingThe `DatasetFactory` now requires the `root_dir` argument to be explicitly provided during instantiation. It was previously optional.

fix

Ensure `DatasetFactory` is called with `DatasetFactory(root_dir='/path/to/data')`. For instance, `DatasetFactory("hf_dataset_id", root_dir="./data")`.

affects: >=0.5.0

breakingThe `hf_hub_id` argument in functions like `load_policy` has been renamed to `repo_id` for consistency with Hugging Face Hub terminology.

fix

Replace `hf_hub_id='your/model'` with `repo_id='your/model'` when loading policies or datasets from the Hugging Face Hub.

affects: >=0.5.0

breakingThe signature and name of the policy sampling method have changed. `policy.sample()` is now `policy.sample_actions(obs)` and requires the current observation as input.

fix

Update calls from `action = policy.sample()` to `action = policy.sample_actions(obs)`.

affects: >=0.4.0

gotchaLeRobot requires Python 3.12+ and PyTorch >= 2.1. Incompatibilities can lead to installation issues or runtime errors.

fix

Ensure your Python environment is `3.12` or newer, and PyTorch is `2.1` or newer. Use `pip install torch` to update PyTorch if needed.

affects: All versions

Upgrade

Version history

0.5.1latest on PyPI · released Apr 7, 2026

Audit

Dependencies

No dependency data recorded yet.

Agent activity

13 hits · last 30 days

node

Resources

githubhttps://github.com/huggingface/lerobot ↗

packagehttps://pypi.org/project/lerobot/ ↗

homepagehttps://huggingface.co/lerobot ↗

docshttps://huggingface.co/docs/lerobot/index ↗