Tactile sensing, skill learning and robotic dexterous manipulation

• Titel: Tactile sensing, skill learning and robotic dexterous manipulation
• Mitwirkende: Li, Qiang [MitwirkendeR]
• Verf.angabe: edited by Qiang Li [and more]
• Verlagsort: London
• Verlag: Academic Press
• Jahr: 2022
• Umfang: 1 online resource
• ISBN: 978-0-323-90417-9
• ISBN: 0-323-90417-3
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe
• Sach-SW: Préhenseurs
• Sach-SW: Robotique
• Sach-SW: Capteurs tactiles
• Sach-SW: Robot hands
• Sach-SW: Robotics
• Sach-SW: Tactile sensors
• K10plus-PPN: 1892763788
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4544472733
• Lokale URL Inst.: Zum Volltext

Abstract

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.