Robot Control, Learning, Perception and Teleoperation
Distinguished Research Seminar Series
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Date
23 Dec 2024
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Organiser
Department of Industrial and Systems Engineering, PolyU
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Time
10:00 - 11:30
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Venue
DE307
Speaker
Prof. Chenguang Yang
Summary
Learning from Demonstration (LfD), or imitation learning, allows robots to acquire and generalize task skills through human demonstrations, creating a seamless integration of machine learning and robotics. Most LfD approaches often overlook the importance of demonstrated forces and rely on manually configured impedance parameters. In response, my team has developed a series of biomimetic impedance and force controllers inspired by neuroscientific findings on motor control mechanisms in humans, enabling robots to imitate compliant manipulation skills. Our models reduce the dimensionality of skill representation, facilitating online optimization and reducing system sensitivity to parameter changes. To improve robot skill learning through enhanced perceptual capabilities, we designed anthropomorphic visual tactile sensors that assess contact force, surface texture, and shape, closely resembling the softness and wear resistance of human fingers for superior manipulation. The control and learning technologies we have developed have been particularly effective in robot teleoperation and human-robot collaboration, with shared control-based semi-autonomous methods that effectively integrate human intent with robotic autonomy, thereby achieving greater efficiency and usability.
Keynote Speaker
Prof. Chenguang Yang
Professor
Department of Computer Science,
University of Liverpool, UK
Professor Chenguang Yang holds the Chair in Robotics in the Department of Computer Science at the University of Liverpool, UK, where he leads the Robotics and Autonomous Systems Group. He is recognized as a Fellow by several prestigious institutions, including the Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), Asia-Pacific AI Association (AAIA), and British Computer Society (BCS). Professor Yang serves as the corresponding Co-Chair of the IEEE Technical Committee on Collaborative Automation for Flexible Manufacturing (CAFM) and holds prominent editorial positions as Editor-in-Chief of Robot Learning, Editor of IEEE Transactions on Automation Science and Engineering, Senior Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Specialty Chief Editor for Computational Intelligence in Robotics for Frontiers in Robotics and AI. He previously served as President of the Chinese Automation and Computing Society in the UK (CACSUK) and has organized several conferences as the general chair, the 25th IEEE International Conference on Industrial Technology (ICIT) and the 27th International Conference on Automation and Computing (ICAC). As the lead author, he received the prestigious IEEE Transactions on Robotics Best Paper Award in 2012 and the IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022.
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