lerobot
🤗 LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch
Decision gist · record as of 2026-08-14
Yes, if you are building or researching real-world robotics systems. LeRobot is actively maintained, has no known vulnerabilities, and provides a mature ecosystem for policy training and hardware integration. The Apache-2.0 license is permissive for commercial use. Install friction is low and the framework is designed to be extensible. Main consideration: it requires Python 3.12+ and substantial ML dependencies (torch, torchvision); ensure your development environment and target hardware can support them.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; torch and torchvision installation may require system-level dependencies or GPU drivers depending on your hardware target.
- Low friction installation as a pure Python wheel.
- Active maintenance with recent releases; 16 runtime dependencies including torch, torchvision, and huggingface-hub are substantial but standard for ML robotics work.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with attribution. No copyleft restrictions; you can integrate into proprietary robotics systems.
last release 2026-08-03 (11 days) · last repo commit 2026-08-14 · 26,654 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 287,058 downloads/mo, #8,036 on PyPI
Alternatives
Verify before relying
pip install lerobot
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
print(dataset[0]['action'].shape)- Actual GPU/CPU memory requirements for training different policy types (ACT, Diffusion, VLAs, etc.)
- Whether custom robot implementations require C++ extensions or are pure Python
- Real-world transfer success rates for the listed policy types on unseen hardware
- Performance overhead of the standardized Robot interface vs. direct hardware APIs
What it is and what it does
LeRobot is a PyTorch-native robotics framework that standardizes control, data, and training across diverse robot hardware and policy types. It provides a unified Robot class interface that abstracts hardware specifics (supporting platforms from SO-100 arms to humanoids like Unitree G1), a standardized LeRobotDataset format (Parquet + video) hosted on Hugging Face Hub for efficient dataset management, and implementations of state-of-the-art policies spanning imitation learning (ACT, Diffusion, VQ-BeT), reinforcement learning (HIL-SERL, TDMPC), vision-language-action models (Pi0, GR00T, SmolVLA), world models, and reward models.
The framework is designed to lower barriers to entry for robotics research and deployment: you can load pretrained policies, train on standardized datasets with a single command, evaluate on benchmarks like LIBERO and MetaWorld, and extend it with custom hardware or policies by implementing the provided interfaces. It integrates tightly with the Hugging Face ecosystem for model and dataset sharing, making it practical for teams building real-world robotic systems or contributing to shared research infrastructure.
Use it for
- Train imitation learning policies on collected robot teleoperation data and deploy them to real hardware for manipulation tasks.
- Load and fine-tune pretrained vision-language-action models for new robot platforms without retraining from scratch.
- Standardize data collection and storage across multiple robot instances using the LeRobotDataset format for reproducible research.
- Evaluate robot policies in simulation (LIBERO, MetaWorld) before deploying to physical hardware.
- Implement and share custom robot hardware drivers by extending the Robot interface and leveraging LeRobot's training and evaluation pipeline.
- Benchmark reinforcement learning policies (HIL-SERL, TDMPC) on standardized robotics tasks across different hardware platforms.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or researching real-world robotics systems.
LeRobot is actively maintained, has no known vulnerabilities, and provides a mature ecosystem for policy training and hardware integration. The Apache-2.0 license is permissive for commercial use. Install friction is low and the framework is designed to be extensible. Main consideration: it requires Python 3.12+ and substantial ML dependencies (torch, torchvision); ensure your development environment and target hardware can support them.
Install
lerobot on PyPI
Before you install
Low friction installation as a pure Python wheel. Active maintenance with recent releases; 16 runtime dependencies including torch, torchvision, and huggingface-hub are substantial but standard for ML robotics work. Requires Python 3.12+.
Requires Python 3.12 or later; torch and torchvision installation may require system-level dependencies or GPU drivers depending on your hardware target.
License in practice
Apache-2.0 permissive license allows commercial and private use with attribution. No copyleft restrictions; you can integrate into proprietary robotics systems.
Quickstart
pip install lerobot
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset("lerobot/aloha_mobile_cabinet")
print(dataset[0]['action'].shape)Verify before relying
- Actual GPU/CPU memory requirements for training different policy types (ACT, Diffusion, VLAs, etc.)
- Whether custom robot implementations require C++ extensions or are pure Python
- Real-world transfer success rates for the listed policy types on unseen hardware
- Performance overhead of the standardized Robot interface vs. direct hardware APIs
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 16 packagestorchtorchvisionnumpyopencv-python-headlessPilloweinopsdraccushuggingface-hubrequestsgymnasiumsafetensorspackagingtermcolortqdmcmakesetuptools |
| Maintenance | Actively maintained 11 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 287,058 / month, #8,036 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Software Development :: Build Tools |
Evidence: lerobot-0.6.1-py3-none-any.whl
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