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Total Size:
19.7 MB
Info Hash:
D622D32D5DBD4C76DF8125A21B9B5CAE005404D6
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Added:
May 29, 2025, 10:45 a.m.
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(Last updated: May 29, 2025, 10:47 a.m.)
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| Liu H. Embodied Multi-agent Systems. Perception, Action and Learning 2025.pdf | 19.7 MB |
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NOTE
SOURCE: Liu H. Embodied Multi-agent Systems. Perception, Action and Learning 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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 is suitable as a reference book for graduate students with a basic knowledge of Machine Learning, as well as for professional researchers interested in robotics and embodied intelligence. 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. Preface Part I Background Embodied Intelligence Embodied Multi-agent System Part II Theory and Methods Perception-Action Loop in Embodied Multi-Agent System Embodied Cooperation in Multi-Agent System Competitive Learning in Embodied Multi-agent System Large Language Model for Embodied Multi-Agent System Part III Applications Simulation Platform for Embodied Collaboration Between Human and Robots Application of Embodied Multi-Agent System Part IV Conclusions Conclusions and Future Directions
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