Keynote Speakers
Prof. Chenguang Yang, University of Liverpool, UK
Speech Title: Robot Control, Learning, Perception and Teleoperation
Abstract: Learning from Demonstration (LfD), or imitation learning, allows robots to acquire and generalize task skills through human demonstrations, creating a seamless integration of artificial intelligence 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.
Bio: 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 a member of European Academy of Sciences and Arts, and he is also 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). 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, including 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.
Prof. Dayi Wang, China Academy of Space Technology, China
Bio: 北京空间飞行器总体设计部科技委主任，国家杰青、国防卓青，973项目技术首席专家、政府特殊津贴专家，长期从事观测、诊断和重构能力定量表征理论方法以及空间飞行器全自主运行技术研究，为我国探月工程和首次火星探测等任务的圆满完成做出重要贡献。获国家技术发明二等奖1项，国家科技进步特等奖1项，国防技术发明和科技进步一等奖6项，以及何梁何利基金科技创新奖、全国创新争先奖、钱学森杰出贡献奖等，入选国家领军人才和国家级百千万人才工程，被党中央和国务院授予“国家卓越工程师”称号。
Prof. Zhunga Liu, Northwestern Polytechnical University, China
Bio: TBA