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.- 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
Table of Contents
Part I: Tactile sensing and perception1. Tactile sensors for dexterous manipulation2. Robotic perception of object properties using tactile sensing3. Multimodal perception for dexterous manipulation4. Using Machine Learning for Material Detection with Capacitive Proximity SensorsPart II: Skill representation and learning5. Admittance control: learning from human and collaboration with human6. Sensorimotor Control for Dexterous Grasping--Inspiration from human hand7. Efficient Haptic Learning and Interaction8. From human to robot grasping: kinematics and forces synergies9. Learning a form-closure grasping with attractive region in environment10. Learning hierarchical control for robust in-hand manipulation11. Learning Industrial Assembly by Guided-DDPGPart III: Robotic hand adaptive control12. The novel poly-articulated prosthetic hand Hannes: A survey study, and clinical evaluation13. Enhancing vision control by tactile sensing for robotic manipulation14. Neural Network enhanced Optimal Control of Manipulator15. Towards Dexterous In-Hand Manipulation of Unknown Objects: A Feedback Based Control Approach16. Learning Industrial Assembly by Guided-DDPG
