Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

1st Edition - April 2, 2022
Editors: Qiang Li, Shan Luo, Zhaopeng Chen, Chenguang Yang, Jianwei Zhang
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 4 4 5 - 2
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 4 1 7 - 9

Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: ta… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Part I: Tactile sensing and perception
Chapter 1: GelTip tactile sensor for dexterous manipulation in clutter
Abstract
Acknowledgement
1.1. Introduction
1.2. An overview of the tactile sensors
1.3. The GelTip sensor
1.4. Evaluation
1.5. Conclusions and discussion
References
Chapter 2: Robotic perception of object properties using tactile sensing
Abstract
2.1. Introduction
2.2. Material properties recognition using tactile sensing
2.3. Object shape estimation using tactile sensing
2.4. Object pose estimation using tactile sensing
2.5. Grasping stability prediction using tactile sensing
2.6. Vision-guided tactile perception for crack reconstruction
2.7. Conclusion and discussion
References
Chapter 3: Multimodal perception for dexterous manipulation
Abstract
Acknowledgement
3.1. Introduction
3.2. Visual-tactile cross-modal generation
3.3. Spatiotemporal attention model for tactile texture perception
3.4. Conclusion and discussion
References
Chapter 4: Capacitive material detection with machine learning for robotic grasping applications
Abstract
4.1. Introduction
4.2. Basic knowledge
4.3. Methods
4.4. Experiments
4.5. Conclusion
References
Part II: Skill representation and learning
Chapter 5: Admittance control: learning from humans through collaborating with humans
Abstract
5.1. Introduction
5.2. Learning from human based on admittance control
5.3. Experimental validation
5.4. Human robot collaboration based on admittance control
5.5. Variable admittance control model
5.6. Experiments
5.7. Conclusion
References
Chapter 6: Sensorimotor control for dexterous grasping – inspiration from human hand
Abstract
Acknowledgements
6.1. Introduction of sensorimotor control for dexterous grasping
6.2. Sensorimotor control for grasping kinematics
6.3. Sensorimotor control for grasping kinetics
6.4. Conclusions
References
Chapter 7: From human to robot grasping: force and kinematic synergies
Abstract
Acknowledgements
7.1. Introduction
7.2. Experimental studies
7.3. Discussion
7.4. Conclusions
References
Chapter 8: Learning form-closure grasping with attractive region in environment
Abstract
8.1. Background
8.2. Related work
8.3. Learning a form-closure grasp with attractive region in environment
8.4. Conclusion
References
Chapter 9: Learning hierarchical control for robust in-hand manipulation
Abstract
9.1. Introduction
9.2. Related work
9.3. Methodology
9.4. Experiments
9.5. Conclusion
References
Chapter 10: Learning industrial assembly by guided-DDPG
Abstract
10.1. Introduction
10.2. From model-free RL to model-based RL
10.3. Guided deep deterministic policy gradient
10.4. Simulations and experiments
10.5. Chapter summary
References
Part III: Robotic hand adaptive control
Chapter 11: Clinical evaluation of Hannes: measuring the usability of a novel polyarticulated prosthetic hand
Abstract
11.1. Introduction
11.2. Preliminary study
11.3. The Hannes system
11.4. Pilot study for evaluating the Hannes hand
11.5. Validation of custom EMG sensors
11.6. Discussion and conclusions
References
Chapter 12: A hand-arm teleoperation system for robotic dexterous manipulation
Abstract
12.1. Introduction
12.2. Problem formulation
12.3. Vision-based teleoperation for dexterous hand
12.4. Hand-arm teleoperation system
12.5. Transteleop evaluation
12.6. Manipulation experiments
12.7. Conclusion and discussion
References
Chapter 13: Neural network-enhanced optimal motion planning for robot manipulation under remote center of motion
Abstract
13.1. Introduction
13.2. Problem statement
13.3. Control system design
13.4. Simulation results
13.5. Conclusion
References
Chapter 14: Towards dexterous in-hand manipulation of unknown objects
Abstract
Acknowledgement
14.1. Introduction
14.2. State of the art
14.3. Reactive object manipulation framework
14.4. Finding optimal regrasp points
14.5. Evaluation in physics-based simulation
14.6. Evaluation in a real robot experiment
14.7. Summary and outlook
References
Chapter 15: Robust dexterous manipulation and finger gaiting under various uncertainties
Abstract
15.1. Introduction
15.2. Dual-stage manipulation and gaiting framework
15.3. Modeling of uncertain manipulation dynamics
15.4. Robust manipulation controller design
15.5. Real-time finger gaits planning
15.6. Simulation and experiment studies
15.7. Chapter summary
References
Appendix A: Key components of dexterous manipulation: tactile sensing, skill learning, and adaptive control
A.1. Introduction
A.2. Why sensing, why tactile sensing
A.3. Why skill learning
A.4. Why adaptive control
A.5. Conclusion
Index

Qiang Li

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.

Affiliations and expertise

Project Investigator, DEXMAN, Germany

Shan Luo

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.

Affiliations and expertise

King’s College London

Zhaopeng Chen

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.

Affiliations and expertise

Professor, University of Hamburg, Faculty of Mathematics, Informatics and Natural Science Department Informatics, Hamburg, Germany

Chenguang Yang

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.

Affiliations and expertise

Professor, Bristol Robotics Lab, UK

Jianwei Zhang

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.

Affiliations and expertise

Professor and Director of TAMS, University of Hamburg, Faculty of Mathematics, Informatics and Natural Science Department Informatics, Hamburg, Germany