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

TL;DR

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, designed for accessibility, scalability, and openness.

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.

Figures (11)

• Figure 1: lerobot is an open-source library for end-to-end robot learning. It covers the entire stack, from middleware motor interfaces to large-scale data collection and dataset streaming, supporting an optimized inference stack, scalable implementations of SOTA robot learning algorithms, and providing support for training custom models as well as easily reusing pre-trained ones.
• Figure 2: Some of the explicit and implicit models for autonomous motion.
• Figure 3: Classical robotics uses modular, model-based pipelines with hand-crafted features, while robot learning employs monolithic, data-driven policies that learn directly from interaction data.
• Figure 4: (A) Low-cost, open-source robots like SO-10X and ALOHA cost a fraction of proprietary industrial arms, using consumer-grade parts and 3D-printable designs. (B) Decentralized efforts to collect expert demonstrations in the form of trajectories surpassed centralized efforts for the collection of large amounts of real-world robotics data.
• Figure 5: Numbers and trends of downloads, datasets, and episodes by robot type over time. The number of episodes in each dataset has been explicitly tracked starting in October 2024 only. For completeness, we report the top-5 robots grouped in Other, for each of the metrics considered, in Table \ref{['tab:sec3-robots-other']}.