Embodied Multi-Agent Systems: Perception, Action, and Learning (Machine Learning: Foundations, Methodologies, and Applications) - Tapa dura

Acerca del autor

Huaping Liu received his Ph.D. degree from Tsinghua University, Beijing, China, in 2004. He is currently a professor in the Department of Computer Science and Technology at Tsinghua University. His research interests include robot perception and learning. Dr. Liu received the National Science Fund for Distinguished Young Scholars and served as the Area Chair for Robotics Science and Systems multiple times. He is a senior editor of the International Journal of Robotics Research. Dr. Liu published the book “Robotic Tactile Perception and Understanding” with Springer in 2018.

Xinzhu Liu received her Ph.D. degree in computer science and technology from Tsinghua University, Beijing, China, in 2024. Her research interests include embodied intelligence, visual perception, and multi-agent collaboration.

Kangyao Huang received his M.Res. in Control and Systems Engineering from the University of Sheffield, Sheffield, U.K., in 2020. He is currently pursuing a Ph.D. degree in computer science and technology at Tsinghua University, Beijing, China. He has interdisciplinary experience and several years of industry experience, providing applied research in cooperation with partners in the information, aerospace, and manufacturing sectors. His research interests include robot learning and swarm robotics.

Di Guo received her Ph.D. degree in Computer Science and Technology from Tsinghua University, Beijing, China, in 2017. She is currently a professor in the School of Artificial Intelligence at Beijing University of Posts and Telecommunications, Beijing. Her research interests include intelligent robots, computer vision, and machine learning.

De la contraportada

In recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems.

This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents—homogeneous, heterogeneous, and ad hoc. Beyond the popular reinforcement learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems.

By interchangeably utilizing constrained optimization, reinforcement learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book will be particularly valuable to graduate students and professional researchers in robotics and machine learning. It provides a robust learning framework for addressing practical challenges in embodied multi-agent systems and demonstrates the promising potential of fundamental models for scenario generation, policy learning, and planning in complex collaboration problems.