Название: Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation
Автор: Edited by Qiang Li, Shan Luo, Zhaopeng Chen, Chenguang Yang, Jianwei Zhang
Издательство: Academic Press
Год: 2022
Страниц: 359
Размер: 15.46 МБ
Формат: PDF
Язык: English
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