Pepijn
3cd348ffe2
Root cause of every collapsed inference run. ``embed_prefix`` flags
all language tokens ``att=0``; ``make_att_2d_masks`` turns that into
a single fully BIDIRECTIONAL block. So during the text-loss forward,
a supervised subtask token's hidden state attends to the very tokens
it is trained to predict. The cross-entropy degenerates into a copy
task — ``text_loss → ~3e-5`` not because the model learned to
predict subtasks but because it can see the answer.
At inference ``select_message`` decodes autoregressively (causally):
each token must be predicted WITHOUT seeing it — a task the model
was never actually trained on. Hence the universal collapse: a
coherent first token or two ("grasp the yellow cube"), then a loop
("cover cover cover", "icatorsicators", "the the the").
Fix: ``_mark_target_span_causal`` sets ``att=1`` on the language
positions that are supervised targets (``text_labels != -100``).
With make_att_2d_masks's cumulative-block rule each target token
then attends to images + the user prompt bidirectionally and to
EARLIER target tokens only — genuine causal next-token prediction,
matching select_message. Applied in both ``_compute_text_loss`` and
``_compute_fused_loss``. Per-sample correct: high_level_subtask
targets become causal; low_level_execution subtasks (a user turn,
labels all -100) stay bidirectional so the action expert reads them
as bidirectional context. The action expert is otherwise unaffected
— the suffix has a strictly higher cumsum and still attends to the
whole prefix.
Requires retraining: this changes the training objective. Existing
checkpoints were all trained on the degenerate copy task and cannot
generate text. Expect ``text_loss`` to settle MUCH higher than 3e-5
after this — that is correct; it is now a real prediction task.
NOTE: pi052's text path (PaliGemma prefix-LM) has the same
bidirectional-block structure and needs the analogous fix.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
LeRobot aims to provide models, datasets, and tools for real-world robotics in PyTorch. The goal is to lower the barrier to entry so that everyone can contribute to and benefit from shared datasets and pretrained models.
🤗 A hardware-agnostic, Python-native interface that standardizes control across diverse platforms, from low-cost arms (SO-100) to humanoids.
🤗 A standardized, scalable LeRobotDataset format (Parquet + MP4 or images) hosted on the Hugging Face Hub, enabling efficient storage, streaming and visualization of massive robotic datasets.
🤗 State-of-the-art policies that have been shown to transfer to the real-world ready for training and deployment.
🤗 Comprehensive support for the open-source ecosystem to democratize physical AI.
Quick Start
LeRobot can be installed directly from PyPI.
pip install lerobot
lerobot-info
Important
For detailed installation guide, please see the Installation Documentation.
Robots & Control
LeRobot provides a unified Robot class interface that decouples control logic from hardware specifics. It supports a wide range of robots and teleoperation devices.
from lerobot.robots.myrobot import MyRobot
# Connect to a robot
robot = MyRobot(config=...)
robot.connect()
# Read observation and send action
obs = robot.get_observation()
action = model.select_action(obs)
robot.send_action(action)
Supported Hardware: SO100, LeKiwi, Koch, HopeJR, OMX, EarthRover, Reachy2, Gamepads, Keyboards, Phones, OpenARM, Unitree G1.
While these devices are natively integrated into the LeRobot codebase, the library is designed to be extensible. You can easily implement the Robot interface to utilize LeRobot's data collection, training, and visualization tools for your own custom robot.
For detailed hardware setup guides, see the Hardware Documentation.
LeRobot Dataset
To solve the data fragmentation problem in robotics, we utilize the LeRobotDataset format.
- Structure: Synchronized MP4 videos (or images) for vision and Parquet files for state/action data.
- HF Hub Integration: Explore thousands of robotics datasets on the Hugging Face Hub.
- Tools: Seamlessly delete episodes, split by indices/fractions, add/remove features, and merge multiple datasets.
from lerobot.datasets.lerobot_dataset import LeRobotDataset
# Load a dataset from the Hub
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
# Access data (automatically handles video decoding)
episode_index=0
print(f"{dataset[episode_index]['action'].shape=}\n")
Learn more about it in the LeRobotDataset Documentation
SoTA Models
LeRobot implements state-of-the-art policies in pure PyTorch, covering Imitation Learning, Reinforcement Learning, and Vision-Language-Action (VLA) models, with more coming soon. It also provides you with the tools to instrument and inspect your training process.
Training a policy is as simple as running a script configuration:
lerobot-train \
--policy=act \
--dataset.repo_id=lerobot/aloha_mobile_cabinet
| Category | Models |
|---|---|
| Imitation Learning | ACT, Diffusion, VQ-BeT, Multitask DiT Policy |
| Reinforcement Learning | HIL-SERL, TDMPC & QC-FQL (coming soon) |
| VLAs Models | Pi0Fast, Pi0.5, GR00T N1.5, SmolVLA, XVLA |
Similarly to the hardware, you can easily implement your own policy & leverage LeRobot's data collection, training, and visualization tools, and share your model to the HF Hub
For detailed policy setup guides, see the Policy Documentation.
Inference & Evaluation
Evaluate your policies in simulation or on real hardware using the unified evaluation script. LeRobot supports standard benchmarks like LIBERO, MetaWorld and more to come.
# Evaluate a policy on the LIBERO benchmark
lerobot-eval \
--policy.path=lerobot/pi0_libero_finetuned \
--env.type=libero \
--env.task=libero_object \
--eval.n_episodes=10
Learn how to implement your own simulation environment or benchmark and distribute it from the HF Hub by following the EnvHub Documentation
Resources
- Documentation: The complete guide to tutorials & API.
- Chinese Tutorials: LeRobot+SO-ARM101中文教程-同济子豪兄 Detailed doc for assembling, teleoperate, dataset, train, deploy. Verified by Seed Studio and 5 global hackathon players.
- Discord: Join the
LeRobotserver to discuss with the community. - X: Follow us on X to stay up-to-date with the latest developments.
- Robot Learning Tutorial: A free, hands-on course to learn robot learning using LeRobot.
Citation
If you use LeRobot in your project, please cite the GitHub repository to acknowledge the ongoing development and contributors:
@misc{cadene2024lerobot,
author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
howpublished = "\url{https://github.com/huggingface/lerobot}",
year = {2024}
}
If you are referencing our research or the academic paper, please also cite our ICLR publication:
ICLR 2026 Paper
@inproceedings{cadenelerobot,
title={LeRobot: An Open-Source Library for End-to-End Robot Learning},
author={Cadene, Remi and Alibert, Simon and Capuano, Francesco and Aractingi, Michel and Zouitine, Adil and Kooijmans, Pepijn and Choghari, Jade and Russi, Martino and Pascal, Caroline and Palma, Steven and Shukor, Mustafa and Moss, Jess and Soare, Alexander and Aubakirova, Dana and Lhoest, Quentin and Gallou\'edec, Quentin and Wolf, Thomas},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://arxiv.org/abs/2602.22818}
}
Contribute
We welcome contributions from everyone in the community! To get started, please read our CONTRIBUTING.md guide. Whether you're adding a new feature, improving documentation, or fixing a bug, your help and feedback are invaluable. We're incredibly excited about the future of open-source robotics and can't wait to work with you on what's next—thank you for your support!
