Description: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection by Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li 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 as anomaly recovery strategies. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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 as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. Back Cover 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 as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. Author Biography Dr. Xuefeng Zhou is an Associate Professor and Leader of the Robotics Team at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Manufacturing and Automation from the South China University of Technology in 2011. His research mainly focuses on motion planning and control, force control and legged robots. He has published more than 40 journal articles and conference papers.Dr. Hongmin Wu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Mechanical Engineering from Guangdong University of Technology, Guangzhou, China, in 2019. His research mainly focuses on robot learning, autonomous manipulation, deep learning and human–robot collaboration. He has published more than 20 journal articles and conference papers.Dr. Juan Rojas is an "100 Young Talents" Associate Professor at the Guangdong University of Technology inGuangzhou, China, where he works at the Biomimetics and Intelligent Robotics Lab (BIRL). Dr. Rojas currently researches robot introspection, human intention prediction, high-level state estimation and skill acquisition for manipulation tasks. He has published more than 40 journal articles and conference papers. Dr. Rojas serves as an Associate Editor of Advanced Robotic Journal since 2019 and Associate Editor of IEEE International Conference on Intelligent Robots and Systems (IROS) since 2017.Dr. Zhihao Xu is a Researcher at Guangdong Institute of Intelligent Manufacturing, Guangdong Academy of Science. He received his Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology, China, in 2016. His research mainly focuses on intelligent control theory, motion planning and control and force control. He has published more than 30 journal articles and conference papers.Prof. Shuai Li is a Ph.D. Supervisor and Associate Professor (Reader) at the College of Engineering, Swansea University, UK. He received his Ph.D. degree in Electrical and Computer Engineering from Stevens Institute of Technology, New Jersey, USA, in 2014. His research interests are robot manipulation, automation and instrumentation, artificial intelligence and industrial robots. He has published over 80 papers in journals such as IEEE TAC, TII, TCYB, TIE and TNNLS. He serves as Editor-in-Chief of the International Journal of Robotics and Control and was the General Co-Chair of the 2018 International Conference on Advanced Robotics and Intelligent Control. Table of Contents Introduction to Robot Introspection.- Nonparametric Bayesian Modeling of Multimodal Time Series.- Incremental Learning Robot Complex Task Representation and Identification.- Nonparametric Bayesian Method for Robot Anomaly Monitoring.- Nonparametric Bayesian Method for Robot Anomaly Diagnose.- Learning Policy for Robot Anomaly Recovery based on Robot. Feature Is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven context, which can be easily integrated into various robotic systems Introduces a fast, accurate, robot anomaly monitoring, diagnosis and recovery scheme for endowing robots with longer-term autonomy and a safer collaborative environment Demonstrates two robots that perform three manipulation tasks: an HIRO-NX robot that performs electronic assembly, and a Baxter robot that performs a pick-and-place task and kitting experiment, providing comprehensive guidance for professional researchers and college students Is an open access book Details ISBN9811562628 Author Shuai Li Language English Year 2020 ISBN-10 9811562628 ISBN-13 9789811562624 Format Hardcover DOI 10.1007/978-981-15-6263-1 Pages 137 Publication Date 2020-07-22 Publisher Springer Verlag, Singapore UK Release Date 2020-07-22 Edition 1st Imprint Springer Verlag, Singapore Place of Publication Singapore Country of Publication Singapore Illustrations 44 Illustrations, color; 6 Illustrations, black and white; XVII, 137 p. 50 illus., 44 illus. in color. 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ISBN-13: 9789811562624
Book Title: Nonparametric Bayesian Learning for Collaborative Robot Multimoda
Number of Pages: 137 Pages
Publication Name: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Language: English
Publisher: Springer Verlag, Singapore
Item Height: 235 mm
Subject: Engineering & Technology, Computer Science, Mathematics
Publication Year: 2020
Type: Textbook
Item Weight: 407 g
Subject Area: Material Science
Author: Shuai Li, Juan Rojas, Hongmin Wu, Zhihao Xu, Xuefeng Zhou
Item Width: 155 mm
Format: Hardcover
