LeRobot: An Open-Source Library for End-to-End Robot Learning

2026-02-26

AI Summary

This work addresses the lack of a unified, open-source, full-stack toolkit in robot learning, which has hindered research efficiency and reproducibility. We present the first end-to-end open-source framework for robot learning that integrates a complete pipeline—from low-level motor control and low-latency communication to asynchronous inference, large-scale data collection, storage, and streaming. The framework supports multiple learning paradigms, including imitation and reinforcement learning, and is compatible with diverse real-world hardware platforms. Emphasizing scalability, real-world applicability, and algorithmic reproducibility, our system significantly lowers the barrier to entry for researchers, enables efficient reproduction of state-of-the-art methods, and achieves stable, scalable end-to-end performance even on low-cost hardware, thereby fostering community collaboration and standardization.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Multiagent Systems: Multiagent Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Robotics is undergoing a significant transformation powered by advances in high-level control techniques based on machine learning, giving rise to the field of robot learning. Recent progress in robot learning has been accelerated by the increasing availability of affordable teleoperation systems, large-scale openly available datasets, and scalable learning-based methods. However, development in the field of robot learning is often slowed by fragmented, closed-source tools designed to only address specific sub-components within the robotics stack. In this paper, we present \texttt{lerobot}, an open-source library that integrates across the entire robot learning stack, from low-level middleware communication for motor controls to large-scale dataset collection, storage and streaming. The library is designed with a strong focus on real-world robotics, supporting accessible hardware platforms while remaining extensible to new embodiments. It also supports efficient implementations for various state-of-the-art robot learning algorithms from multiple prominent paradigms, as well as a generalized asynchronous inference stack. Unlike traditional pipelines which heavily rely on hand-crafted techniques, \texttt{lerobot} emphasizes scalable learning approaches that improve directly with more data and compute. Designed for accessibility, scalability, and openness, \texttt{lerobot} lowers the barrier to entry for researchers and practitioners to robotics while providing a platform for reproducible, state-of-the-art robot learning.

Problem

• robot learning
• open-source
• end-to-end
• scalable learning
• reproducibility

Innovation

• end-to-end robot learning
• open-source robotics library
• scalable learning-based control
• large-scale dataset streaming
• generalized asynchronous inference