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.
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Code: \textbf{\url{https://github.com/huggingface/lerobot}}
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Date: \textbf{\today}