{"product_id":"9789819658732","title":"Embodied Multi-Agent Systems Perception, Action, and Learning","description":"\u003ch3\u003eMachine Learning: Foundations, Methodologies, and Applications\u003c\/h3\u003e\u003ch1\u003eEmbodied Multi-Agent Systems\u003c\/h1\u003e\u003ch2\u003ePerception, Action, and Learning\u003c\/h2\u003e\u003ch3\u003eHuaping Liu | Xinzhu Liu | Kangyao Huang | Di Guo\u003c\/h3\u003e\u003cdiv\u003e\u003cb\u003eComputers \/ Artificial Intelligence \/ General\u003c\/b\u003e\u003c\/div\u003e\u003cbr\u003e\u003cdiv\u003e\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eIn 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eThis 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eBy 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cdiv\u003e\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eHuaping Liu\u003c\/span\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eXinzhu Liu\u003c\/span\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eKangyao Huang\u003c\/span\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\r\n\u003cp style=\"text-align: justify; text-justify: inter-ideograph;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003eDi Guo\u003c\/span\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 11.0pt; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-theme-font: minor-latin;\"\u003e 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.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/div\u003e\u003cbr\u003e\u003ctable\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublication Date: \u003c\/td\u003e\n\u003ctd\u003e23 May 2026\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePublisher: \u003c\/td\u003e\n\u003ctd\u003eSpringer Nature Singapore\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eImprint: \u003c\/td\u003e\n\u003ctd\u003eSpringer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eISBN-13: \u003c\/td\u003e\n\u003ctd\u003e9789819658732\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFormat: \u003c\/td\u003e\n\u003ctd\u003ePaperback \/ softback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePage Count: \u003c\/td\u003e\n\u003ctd\u003e229\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/table\u003e","brand":"Springer Nature Singapore","offers":[{"title":"Default Title","offer_id":50805788344460,"sku":"9789819658732","price":179.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9789819658732.jpg?v=1781792374","url":"https:\/\/lateknightbooks.com\/products\/9789819658732","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}