Robot Learning: A Tutorial

2025-10-14

AI Summary

Robotics learning is undergoing a paradigm shift from model-driven to data-driven approaches, yet lacks a systematic pedagogical framework bridging classical control theory and modern learning-based methods. To address this, we introduce an open-source educational initiative built upon the Lerobot library, unifying reinforcement learning, behavioral cloning, language-conditioned modeling, and deep neural networks into a reproducible experimental and deployment platform. Our framework is uniquely designed to be cross-task, cross-morphology, and language-driven, enabling multi-platform transfer and end-to-end policy training. It delivers comprehensive tutorials, standardized benchmarks, and production-ready code examples—substantially lowering barriers to research and engineering practice. The project provides a scalable, empirically verifiable methodology for both robotics education and real-world technology deployment. (149 words)

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 $ exttt{lerobot}$.

Problem

• Transitioning from model-based to data-driven robot learning methods
• Developing generalist language-conditioned models for diverse robotic tasks
• Providing practical tools and examples for robot learning implementation

Innovation

• Transition from model-based to data-driven robot learning
• Utilize reinforcement learning and behavioral cloning techniques
• Develop generalist language-conditioned models for robots