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Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation
Helps professionals improve robotic dexterity by skill learning, intelligent perception and adaptive control
Qiang Li (Edited by), Shan Luo (Edited by), Zhaopeng Chen (Edited by), Chenguang Yang (Edited by), Jianwei Zhang (Edited by)
9780323904452
Paperback / softback, published 7 April 2022
372 pages, Approx. 100 illustrations (100 in full color)
22.9 x 15.2 x 2.4 cm, 0.59 kg
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.
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
Subject Areas: Machine learning [UYQM], Artificial intelligence [UYQ], Robotics [TJFM1], Electrical engineering [THR], Mechanical engineering [TGB]
