Aiguo Song, Professor
Southeast University, China
Biography: Aiguo Song, Chief professor at Southeast University, Winner of the National Science Fund for Distinguished Young Scholars. He graduated from Southeast University in 1996 with a major in Precision Instrumentation and received Ph.D Degree. Currently, he serves as the dean of the Institute of Space Science and Technology at Southeast University, and the director of the Jiangsu Province Key Laboratory for Robot Perception and Control Technology. He has been engaged in research on robot perception and control technology. He serves as Executive Director of the China Instrument and Control Society, Executive Director of the China Metrology and Testing Society, Chairman of the IEEE Nanjing Robotics Chapter, and President of the Jiangsu Instrument and Control Society. He has won the second prize of the National Technological Invention Award, three times the first prizes of the Technological Invention Award from the Ministry of Education, three times the first prizes of the Jiangsu Provincial Science and Technology Progress Award, and two times the first prizes of the Wu Wenjun Artificial Intelligence Science and Technology Progress Award, and 2007 China Youth Science and Technology Award. He has published over 500+ journal papers, which have been cited more than 18,500 times, and has authored 4 IEEE Standards and 7 Chinese National Standards.
Speech Title: Power Station Inspection and Maintenance Robot
Abstract: Power station inspection and maintenance robots are crucial for ensuring the safe operation of energy facilities in the power station. Due to the complexity of the environments in which electric power facilities are located and the diversity of inspection and maintenance tasks, power station inspection and maintenance robots face challenges such as difficult access to complex scenes, visual perception difficulties caused by complex working conditions, and the difficulty in completing uncertain and variable inspection tasks. This report analyzes the key technologies of power station inspection and maintenance robots, introduces our research works, and discusses the applications of the core technological achievements in the inspection and maintenance of the power stations.
Dan Zhang, Professor
Director of PolyU-Nanjing Technology and Innovation Research Institute, China
Biography: Professor Dan Zhang is a Fellow of the Canadian Academy of Engineering, a Fellow of the Engineering Institute of Canada, a Fellow of the American Society of Mechanical Engineers, and a Fellow of the Canadian Society for Mechanical Engineering. He currently serves as Chair Professor of Intelligent Robotics and Automation at The Hong Kong Polytechnic University, Director of the Research Centre for Intelligent Robotics, and President of The Hong Kong Polytechnic University (Nanjing) Technology and Innovation Research Institute. Professor Zhang has twice been awarded the prestigious Canada Research Chair. He has also received numerous honors, including the Ontario Premier’s Award, the Tier 1 York Research Chair in Advanced Robotics and Mechatronics, the Lassonde Innovation Award, and the Chang Jiang Scholars Program Chair Professorship. He has long been dedicated to the fields of intelligent robotics and automation and is internationally recognized as a leading scholar in parallel robotics and modern manufacturing systems. His main research interests include the synthesis and optimization of parallel and hybrid mechanisms, generalized parallel robots, reconfigurable robots, intelligent biomedical instruments, aerial and underwater robots, embodied intelligence, and AI-robotics systems. To date, he has published more than 516 high-quality academic papers and 12 scholarly monographs. His work has received over 13,632 citations on Google Scholar, with an H-index of 55. Since 2020, he has been listed for six consecutive years among Stanford University’s “World’s Top 2% Scientists.” He holds more than 40 Chinese patents, has led the development of multiple core technologies and their clinical and industrial applications, and has served as principal investigator for research projects totaling more than CAD 26 million. His work has made significant international impact in both academic research and engineering applications.
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Chenguang Yang, Professor
The Hong Kong Polytechnic University, China
Biography: Chenguang (Charlie) Yang is a Professor with the Department of Computing at The Hong Kong Polytechnic University, Previously, he held professorships at University of Liverpool, University of the West of England (UWE Bristol) as well as South China University of Technology. He holds fellowships with Institute of Electrical and Electronics Engineers (IEEE), Institute of Engineering and Technology (IET), Institution of Mechanical Engineers (IMechE), and Aisa-Pacific AI Association (AAIA). He is a member of European Academy of Sciences and Arts (EASA) and a member of National Academy of Artificial Intelligence (NAAI). Professor Yang was selected as a Featured Author on IEEE Xplore in 2023. As lead author, he received the IEEE Transactions on Robotics Best Paper Award in 2012 and IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award in 2022. He also was a lead contributor to the 1st Prize of Chinese Association of Automation (CAA) Natural Science Award in 2022. His current research focuses on embodied AI for robot learning, human robot interaction, and intelligent system design.
Speech Title: Human-like Robot Control, Skill Learning, and Human–Robot Collaboration
Abstract: The presenter proposed a safe contact-control method with humanoid-like compliance adjustment, developed a generalization framework for skill learning in force–position coupled manipulation, investigated optimization mechanisms for personalized collaboration strategies, and developed human–robot collaboration techniques with dynamic adaptability. Addressing core bottlenecks in force-contact tasks—namely the lack of coupled representation of force and position information and the separation between skill learning and low-level control—he proposed a multi-modal, comprehensive skill primitive system covering motion, force control, stiffness, and manipulability, and established a unified skill representation for force–position coupled manipulation. The presenter’s work integrates adaptive control into skill learning algorithms, fully leveraging adaptive control’s ability to compensate for uncertainties and thereby enhancing skill generalization in previously unknown scenarios.