Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation

Herausgegeben:Li, Qiang; Luo, Shan; Chen, Zhaopeng; Yang, Chenguang; Zhang, Jianwei

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Academic Press / Elsevier Science & Technology
• Artikelnr. des Verlages: C2020-0-02663-0
• Englisch
• Abmessung: 17mm x 152mm x 229mm
• Gewicht: 590g
• ISBN-13: 9780323904452
• Artikelnr.: 62605478

Autorenporträt

Dr. Qiang Li received his PhD in Pattern Recognition and Intelligence Systems from Shenyang Institute of Automation(SIA), Chinese Academy of Sciences (CAS) in 2010. He was awarded the stipend from the Honda Research Institute and started his postdoctoral researching at CoR-Lab of Bielefeld University from 2009 to 2012. Currently, he is a Project Investigator of ?DEXMAN? sponsored by Deutsche Forschungsgemeinschaft(DFG) and working in the neuroinformatics group at Bielefeld University. His research interests include: tactile servoing and recognition, sensory-based robotic dexterous manipulation and robotic calibration and dynamic control. He serves as Associate Editor in International Journal of Humanoid Robotics (Robotics) and Complex & Intelligent Systems (AI) and Associated Editor for top level robotics conferences-ICRA, IROS, Humanoids.

Inhaltsangabe

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