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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.
Tactile sensing and perception
GelTip tactile sensor for dexterous manipulation in clutter
Robotic perception of object properties using tactile sensing
Multimodal perception for dexterous manipulation
Capacitive material detection with machine learning for robotic grasping applications
Skill representation and learning
Admittance control: learning from humans through collaborating with humans
Sensorimotor control for dexterous grasping – inspiration from human hand
From human to robot grasping: force and kinematic synergies: Close comparison between human and robotic hands in both force and kinematic domain
Learning form-closure grasping with attractive region in environment
Learning hierarchical control for robust in-hand manipulation
Learning industrial assembly by guided-DDPG
Robotic hand adaptive control
Clinical evaluation of Hannes: measuring the usability of a novel polyarticulated prosthetic hand
A hand-arm teleoperation system for robotic dexterous manipulation
Neural network-enhanced optimal motion planning for robot manipulation under remote center of motion
Towards dexterous in-hand manipulation of unknown objects: A visuotactile feedback-based method
Robust dexterous manipulation and finger gaiting under various uncertainties