Authors:
- 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
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Table of contents (6 chapters)
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Front Matter
About this book
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.
Authors and Affiliations
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Robotic Team Guangdong Institute of Intelligent Manufacturing, Guangzhou, China
Xuefeng Zhou, Hongmin Wu, Zhihao Xu
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School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China
Juan Rojas
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School of Engineering, Swansea University, Swansea, UK
Shuai Li
About the authors
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.Bibliographic Information
Book Title: Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Authors: Xuefeng Zhou, Hongmin Wu, Juan Rojas, Zhihao Xu, Shuai Li
DOI: https://doi.org/10.1007/978-981-15-6263-1
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2020
Hardcover ISBN: 978-981-15-6262-4Published: 22 July 2020
Softcover ISBN: 978-981-15-6265-5Published: 18 September 2020
eBook ISBN: 978-981-15-6263-1Published: 21 July 2020
Edition Number: 1
Number of Pages: XVII, 137
Number of Illustrations: 6 b/w illustrations, 44 illustrations in colour
Topics: Robotics and Automation, Bayesian Inference, Control, Robotics, Mechatronics, Machine Learning, Mathematical Modeling and Industrial Mathematics