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Embodied multi-agent systemsperception, action, and learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Embodied multi-agent systemsby Huaping Liu ... [et al.].
Reminder of title:	perception, action, and learning /
other author:	Liu, Huaping.
Published:	Singapore :Springer Nature Singapore :2025.
Description:	xxviii, 229 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-96-5871-8
ISBN:	9789819658718$q(electronic bk.)

Embodied multi-agent systemsperception, action, and learning /
Embodied multi-agent systemsperception, action, and learning /[electronic resource] :by Huaping Liu ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xxviii, 229 p. :ill., digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

Part I. Background -- Chapter 1. Embodied Intelligence -- Chapter 2. Embodied Multi-agent System -- Part II. Theory and Methods -- Chapter 3. Perception-Action Loop in Embodied Multi-agent System -- Chapter 4. Embodied Cooperation in Multi-agent System -- Chapter 5. Competitive Learning in Embodied Multi-agent System -- Chapter 6. Large Language Model for Embodied Multi-agent System -- Part III. Applications -- Chapter 7. Simulation Platform for Embodied Collaboration between Human and Robots -- Chapter 8. Application of Embodied Multi-agent System -- Part IV. Conlusions -- Chapter 9. Conclusions and Future Directions.

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.

ISBN: 9789819658718$q(electronic bk.)

Standard No.: 10.1007/978-981-96-5871-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: QA76.76.I58 / L58 2025

Dewey Class. No.: 629.892631

Embodied multi-agent systemsperception, action, and learning /
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505 0 $a Part I. Background -- Chapter 1. Embodied Intelligence -- Chapter 2. Embodied Multi-agent System -- Part II. Theory and Methods -- Chapter 3. Perception-Action Loop in Embodied Multi-agent System -- Chapter 4. Embodied Cooperation in Multi-agent System -- Chapter 5. Competitive Learning in Embodied Multi-agent System -- Chapter 6. Large Language Model for Embodied Multi-agent System -- Part III. Applications -- Chapter 7. Simulation Platform for Embodied Collaboration between Human and Robots -- Chapter 8. Application of Embodied Multi-agent System -- Part IV. Conlusions -- Chapter 9. Conclusions and Future Directions.
520 $a 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.
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