机器人学习：教程指南

摘要

机器人学习正处于一个转折点，这一转变由机器学习的快速进步和大规模机器人数据的日益普及所推动。从传统的基于模型的方法转向数据驱动、基于学习的新范式，正在为自主系统解锁前所未有的能力。本教程将引领读者探索现代机器人学习的全景，从强化学习和行为克隆的基础原理出发，直至能够跨多种任务甚至不同机器人实体操作的通才型、语言条件模型。本文旨在为研究人员和从业者提供指南，我们的目标是使读者掌握必要的概念理解和实用工具，以便为机器人学习的发展做出贡献，并通过lerobot中实现的即用型示例加以实践。

English

Robot learning is at an inflection point, driven by rapid advancements in machine learning and the growing availability of large-scale robotics data. This shift from classical, model-based methods to data-driven, learning-based paradigms is unlocking unprecedented capabilities in autonomous systems. This tutorial navigates the landscape of modern robot learning, charting a course from the foundational principles of Reinforcement Learning and Behavioral Cloning to generalist, language-conditioned models capable of operating across diverse tasks and even robot embodiments. This work is intended as a guide for researchers and practitioners, and our goal is to equip the reader with the conceptual understanding and practical tools necessary to contribute to developments in robot learning, with ready-to-use examples implemented in lerobot.

机器人学习：教程指南

Robot Learning: A Tutorial

摘要

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