Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation - Softcover

Softcover

ISBN 10: 0323904459 ISBN 13: 9780323904452

Publisher: Academic Press, 2022

Synopsis

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches.

The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning.

• Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation
• Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control
• Introduces recent work on human’s dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration
• Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches

About this title

About the Authors

Dr. Qiang Li is a full professor at Shenzhen Technology University, China. He received his PhD in Pattern Recognition and Intelligence Systems from the Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), in 2010. He was awarded a stipend from the Honda Research Institute and conducted his postdoctoral research at the CoR-Lab of Bielefeld University from 2009 to 2012. From 2020 to 2023, he served as Principal Investigator at the University of Hamburg and the University of Bielefeld. Dr. Li’s research interests include tactile servoing and recognition, sensory-based robotic

dexterous manipulation, and robotic calibration and dynamic control. He has co-authored over 70 peer reviewed publications in the fields of robotic control, grasping, and manipulation, as well as one edited book. He serves as an Associate Editor for the International Journal of Humanoid Robotics (Robotics) and Complex & Intelligent Systems (AI), and as an Associate Editor for top-tier robotics conferences, including ICRA, IROS, and Humanoids.

Dr Shan Luo is an Associate Professor in the Department of Engineering at King’s College London, where he leads the Robot Perception Lab (RPL). Shan received a Ph.D. from King’s College London for his work on robotic perception through tactile images. In 2016, he visited the MIT Computer and Artificial Intelligence Laboratory (CSAIL). He worked as a Postdoctoral Research Fellow at the University of Leeds and Harvard University, followed by a Lecturer (Assistant Professor) position at the University of Liverpool from 2018 to 2021. His current research focuses on developing intelligent robots capable of safe and agile interaction with the physical environment. His primary interests lie in visuo-tactile sensors, machine learning models for visual and tactile representation learning, and robotic manipulation of challenging objects like deformable and transparent items. He received the EPSRC New Investigator Award in 2021 and a UK-RAS Early Career Award in 2023.

Prof. Dr. Zhaopeng Chen is CEO and founder of Agile Robots AG, which is one of the fastest growing high-tec robotics companies in Germany. He is also a professor in Department of Informatics, University of Hamburg, serving as part of the faculty of Mathematics, Informatics, and Natural Sciences. He was working as Lab Deputy Head in Institute of Robotics and Mechatronics, German Aerospace Center (DLR) for over 10 years. He was leading and working on many robotics projects, including DLRESA Mars rover ground test robotic system, DLR/HIT II dexterous robotic hand system, DLR robot astronaut Rollin’ Justin, et al. The robot he designed has been sent to the space station and is working till now. Prof. Dr. Chen has published over 30 academic papers, and received 2 best paper rewards. He is currently leading 2 European Projects, and 1 DFG projects, and supervising PhD students.

Dr. Chenguang Yang is a Professor of Robotics with University of the West of England, and leader of Robot Teleoperation Group at the Bristol Robotics Laboratory. He received his Ph.D. degree in control engineering from the National University of Singapore in 2010, and postdoctoral training in human robotics from Imperial College London, U.K. His research interests lie in human–robot interaction and intelligent system design. Dr. Yang was awarded the EU Marie Curie International Incoming Fellowship, the U.K. EPSRC UKRI Innovation Fellowship, and the Best Paper Award of IEEE TRANSACTIONS ON ROBOTICS as well as over ten international conference best paper awards. He is a Co-Chair of the Technical Committee on Bio-Mechatronics and Bio-Robotics Systems, IEEE Systems, Man, and Cybernetics Society; and a Co-Chair of the Technical Committee on Collaborative Automation for Flexible Manufacturing, IEEE Robotics and Automation Society. He serves as an Associate Editor of a number of IEEE Transactions and other international leading journals.

Jianwei Zhang is professor and director of TAMS, Department of Informatics, Universität Hamburg, Germany. He is Distinguised Visiting Professor of Tsinghua University, China. He received both his Bachelor of Engineering (1986, with Distinction) and Master of Engineering (1989) at the Department of Computer Science of Tsinghua University, Beijing, China, his PhD (1994) at the Institute of Real-Time Computer Systems and Robotics, Department of Computer Science, University of Karlsruhe, Germany, and Habilitation (2000) at the Faculty of Technology, University of Bielefeld, Germany. His research interests are sensor fusion, intelligent robotics and multimodal machine learning, cognitive computing of Industry4.0, etc. In these areas he has published about 400 journal and conference papers, technical reports, six book chapters and three research monographs. He is the coordinator of the DFG/NSFC Transregional Collaborative Research Centre SFB/TRR169 “Crossmodal Learning”, and several EU robotics projects. He has received multiple best paper awards. He is the General Chairs of IEEE MFI 2012, IEEE/RSJ IROS 2015, and the International Symposium of Human-Centered Robotics and Systems 2018. Jianwei Zhang is life-long Academician of Academy of Sciences in Hamburg.

From the Back Cover

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on the cross-disciplinary lines of research and ground-breaking research ideas on three research lines: tactile sensing, skill learning and dexterous control.

The book introduces the recent work about human’s dexterous skill representation and learning; tactile sensing and its applications on unknown objects’ property recognition and reconstruction. It also introduces the adaptive control schema and its learning by imitation and exploration. The book describes the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in the related branches. The book summarizes the different approaches and discusses the pros and cons of each, and the chapters not only describe the research but also include basic knowledge which can help readers to understand the proposed work.

It also gives insight into recent representative results from different research branches, a whole picture about the state-of-the-art and potential future research directions for robotic dexterous manipulation. It reveals and illustrates how robots can improve its dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches.

This book is an excellent resource for researchers and professionals who work in the robotics industry, haptics and machine learning.

Edition details

• Publisher: Academic Press
• Publication date: 2022
• Language: English
• ISBN 10: 0323904459
• ISBN 13: 9780323904452
• Binding: Paperback
• Number of pages: 372
• Editor: Li, Luo, Chen, Yang, Zhang