Talk Title: Robot Skill Learning and Human-like Control Abstract: Expressing, learning and reusing skills as modularized ones can strengthen the generalization ability of skills and reusability. Human-Robot shared control combines the advantages of both human and robot. This talk will introduce our advance in the field of robot skill learning and human-robot shared control. We use control theory to model the control mechanism of motor neurons to assist us developing human-like robot controllers so that the robot can realize variable impedance control to adaptively physically-interact with the changing environment. We further propose a multi-task impedance control and impedance learning method used on a human-like manipulator with redundant degrees of freedom to achieve compliant human-robot interaction motor control. Learning from human demonstration methods are generally used to efficiently transfer modularized skills to robots using multi-modal information such as surface electromyography signals and contact forces, enhancing the effectiveness of skill reproduction in different situations. We have also developed an enhanced neural-network shared control system for teleoperation, which uses the redundancy of joint space to avoid collisions automatically. The operator does not need to pay attention to possible collisions during manipulation. Besides, with the help of deep learning, we designed a tool power compensation system for teleoperation surgery, thereby enhancing the performance of the force and motion tracking at both ends of the teleoperation system. Furthermore, this talk will also introduce our research on the topics of human-robot collaboration and skill generalization. Biography: Professor Chenguang Yang is the leader of Robot Teleoperation Group and Professor of Robotics at Bristol Robotics Laboratory, UK. He received the Ph.D. degree from the National University of Singapore, Singapore, in 2010, and postdoctoral training in human robotics from the Imperial College London, London, U.K. He was awarded UK EPSRC UKRI Innovation Fellowship (2018) and individual EU Marie Curie International Incoming Fellowship (2011). He is supervisor of an H2020 Marie Sklodowska-Curie Standard European Fellow (2021-2022). As lead author, he won the IEEE Transactions on Robotics Best Paper Award (2012) and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award (2022). He was titled Highly Cited Researcher 2019 by Web of Science. He is a Fellow of British Computer Society and a Fellow of Higher Education Academy. He is a Co-Chair of IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM) and a Co-Chair of IEEE Technical Committee on Bio-mechatronics and Bio-robotics Systems (B2S). He serves as Associate Editors of a number of international top journals including Neurocomputing and seven IEEE Transactions.