Programming by Demonstration: Intuitive Techniques for Robotic Skill Acquisition and Adaptation

"Programming by Demonstration" is an essential resource for those interested in the rapidly advancing field of robotics. Written by Fouad Sabry, this book bridges theoretical knowledge with practical applications in robotic programming. Whether you're a professional, an undergraduate or graduate student, or an enthusiast, this comprehensive guide is designed to enhance your understanding and skills in robotics and automation.

Programming by demonstration: This chapter introduces the fundamental concept of programming by demonstration, focusing on the role of human guidance in robot learning.

Humanoid robot: Explores the design and development of humanoid robots, their challenges, and the significance of their lifelike movements and interactions.

Reinforcement learning: Discusses how reinforcement learning techniques empower robots to learn from their actions, making them adaptive and capable of handling complex tasks.

Developmental robotics: Focuses on the developmental processes in robotics, where robots learn progressively, much like human development, through interaction and feedback.

Human–robot interaction: This chapter delves into the various methods of interaction between humans and robots, emphasizing safety, efficiency, and the potential for collaboration.

Robot learning: Explores different learning paradigms in robotics, including supervised and unsupervised learning, and their application to realworld robotic systems.

Programming by example: Introduces programming by example as a form of teaching robots specific tasks by showing them how to perform actions directly.

Adaptable robotics: Investigates the adaptability of robots in dynamic environments and how they can modify their behavior based on new data or tasks.

Legged robot: Focuses on legged robots and their unique challenges, such as balance, locomotion, and interaction with various terrains.

Offline learning: Covers offline learning methods that allow robots to be trained without realtime interaction, improving their efficiency and reducing training costs.

Apprenticeship learning: Discusses the apprenticeship learning model, where robots learn from expert demonstrations to mimic complex behaviors.

Surena (robot): Provides a detailed look at Surena, a humanoid robot developed in Iran, showcasing its capabilities and the innovations behind its design.

Juggling robot: Describes a robot capable of performing complex tasks like juggling, highlighting the challenges and solutions in balancing dynamic motion.

Cloud robotics: Explores how cloud computing is integrated into robotics, enabling robots to share data and computational resources for better performance.

Incremental learning: Focuses on incremental learning techniques, allowing robots to continuously improve their abilities without forgetting previous knowledge.

Jan Peters (computer scientist): Highlights the work of Jan Peters, a pioneer in robotics, and discusses his contributions to learning and robot development.

Deep reinforcement learning: Introduces deep reinforcement learning, a cuttingedge approach where robots improve their decisionmaking capabilities through neural networks.

Aude Billard: A look at Aude Billard's groundbreaking research in humanrobot interaction and robot learning, emphasizing her impact on the field.

Auke Ijspeert: Discusses the work of Auke Ijspeert, particularly his contributions to robotic locomotion and braininspired robotic control.

Imitation learning: Focuses on imitation learning, a process where robots learn tasks by observing human behavior, a powerful tool for skill transfer.

Robot: Concludes with an exploration of robots in general, covering their history, development, and future potential in various industries.