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 hands-on tools necessary to understand and contribute to developments in robot learning.\

Code: **[https://github.com/huggingface/lerobot](https://github.com/huggingface/lerobot)**\
Date: **2025-09-17**