Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well
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ic Bayesian Learning for Collaborative Robot Multimodal Introspection
Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Xuefeng Zhou Hongmin Wu Juan Rojas Zhihao Xu Shuai Li •
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Xuefeng Zhou Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China
Hongmin Wu Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China
Juan Rojas School of Electromechanical Engineering Guangdong University of Technology Guangzhou, China
Zhihao Xu Robotic Team Guangdong Institute of Intelligent Manufacturing Guangzhou, Guangdong, China
Shuai Li School of Engineering Swansea University Swansea, UK
ISBN 978-981-15-6262-4 ISBN 978-981-15-6263-1 https://doi.org/10.1007/978-981-15-6263-1
(eBook)
© The Editor(s) (if applicable) and The Author(s) 2020. This book is an open access publication. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
To our ancestors and parents, as always
Preface
Improving collaborative robot safety by data-driven monitoring and protection is critical for robots to be actively useful in human daily life. While some success has been demonstrated in structured and static environments that robots are slow, clumsy, and not ge
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