Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

1st Edition - April 2, 2022

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  • Editors: Qiang Li, Shan Luo, Zhaopeng Chen, Chenguang Yang, Jianwei Zhang
  • eBook ISBN: 9780323904179
  • Paperback ISBN: 9780323904452

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Description

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.

Key Features

  • 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

Readership

Researchers and graduate (PhD) students in tactile sensing, machine learning and control domains, robotics, computer science, psychophysics, biomechanics, skill learning and dexterous control. Senior undergraduates and graduates in robotics, tactile sensing, machine learning and control domains, neuroscience, computer science

Table of Contents

  • 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

Product details

  • No. of pages: 372
  • Language: English
  • Copyright: © Academic Press 2022
  • Published: April 2, 2022
  • Imprint: Academic Press
  • eBook ISBN: 9780323904179
  • Paperback ISBN: 9780323904452

About the Editors

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

Shan Luo is a Lecturer (Assistant Professor) at the Department of Computer Science, University of Liverpool, and leads the smARTLab at the Department. Previous to Liverpool, he was a Research Fellow at Harvard University and University of Leeds. He was also a Visiting Scientist at the Computer Science and Artificial Intelligence Laboratory (CSAIL), MIT. He received the B.Eng. degree in Automatic Control from China University of Petroleum, Qingdao, China, in 2012. He was awarded the Ph.D. degree in Robotics from King’s College London, UK, in 2016. His research interests include tactile sensing, robot learning and computer vision.

Affiliations and Expertise

Assistant Professor, Department of Computer Science, University of Liverpool, UK

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

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