Robot Learning

For decades, traditional robotics relied heavily on meticulously hand-engineered control systems. Robots were programmed to perform specific tasks in highly structured environments. However, the past decade has witnessed a paradigm shift fuelled by advancements in artificial intelligence, particularly in areas like deep learning and reinforcement learning. We've moved from programming robots to learning robots. This book is the definitive guide for researchers, engineers, and graduate students who want to design robots that see, think, and act with unprecedented autonomy.

The book opens with a rigorous yet accessible foundation in robot kinematics, dynamics, and learning paradigms. It journeys through four major robotic platforms:

• Ground robots vision based navigation, robust traversability mapping, multi goal exploration, and hierarchical parking.
• Legged robots bio inspired locomotion, hierarchical reinforcement for complex gaits, continuous learning, and whole body control for humanoids.
• Manipulation learning from visual demonstrations, grasp stacking, mobile manipulation, large language model augmented policies, and automated welding.
• Aerial vehicles observational learning for patrol, target tracking, and reinforcement driven control.

Each chapter blends theory with detailed case studies. Whether you re an academic researcher building the next generation of legged robots, a developer deploying autonomous ground vehicles, or an engineer turning a prototype into a product, this book offers the theory, algorithms, and practical case studies you need to bring robot learning to bear on real robotic systems.