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.
Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.
Table of Contents
Part I: Tactile sensing and perception 1. Tactile sensors for dexterous manipulation 2. Robotic perception of object properties using tactile sensing 3. Multimodal perception for dexterous manipulation 4. Using Machine Learning for Material Detection with Capacitive Proximity Sensors
Part II: Skill representation and learning 5. Admittance control: learning from human and collaboration with human 6. Sensorimotor Control for Dexterous Grasping--Inspiration from human hand 7. Efficient Haptic Learning and Interaction 8. From human to robot grasping: kinematics and forces synergies 9. Learning a form-closure grasping with attractive region in environment 10. Learning hierarchical control for robust in-hand manipulation 11. Learning Industrial Assembly by Guided-DDPG
Part III: Robotic hand adaptive control 12. The novel poly-articulated prosthetic hand Hannes: A survey study, and clinical evaluation 13. Enhancing vision control by tactile sensing for robotic manipulation 14. Neural Network enhanced Optimal Control of Manipulator 15. Towards Dexterous In-Hand Manipulation of Unknown Objects: A Feedback Based Control Approach 16. Learning Industrial Assembly by Guided-DDPG
Authors
Qiang Li Project Investigator, DEXMAN, Germany.
Dr. Qiang Li received his PhD in Pattern Recognition and Intelligence Systems from Shenyang Institute of Automation(SIA), Chinese Academy of Sciences (CAS) in 2010. He was awarded the stipend from the Honda Research Institute and started his postdoctoral researching at CoR-Lab of Bielefeld University from 2009 to 2012. Currently, he is a Project Investigator of �DEXMAN� sponsored by Deutsche Forschungsgemeinschaft(DFG) and working in the neuroinformatics group at Bielefeld University. His research interests include: tactile servoing and recognition, sensory-based robotic dexterous manipulation and robotic calibration and dynamic control. He serves as Associate Editor in International Journal of Humanoid Robotics (Robotics) and Complex & Intelligent Systems (AI) and Associated Editor for top level robotics conferences-ICRA, IROS, Humanoids.
Shan Luo King's College London.
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.
Zhaopeng Chen Professor, University of Hamburg, Faculty of Mathematics, Informatics and Natural Science Department Informatics, Hamburg, Germany.
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.
Chenguang Yang Professor, Bristol Robotics Lab, UK.
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 Professor and Director of TAMS, University of Hamburg, Faculty of Mathematics, Informatics and Natural Science Department Informatics, Hamburg, Germany.
Jianwei Zhang is professor and director of TAMS, Department of Informatics, Universit�t 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.
