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. Zhunga Liu, Northwestern Polytechnical University, China
Speech Title: Multi-Source Information Fusion for Airborne Target Recognition
Abstract：Visual perception plays a crucial role in enabling autonomous flight for unmanned aerial vehicles. However, visual navigation faces challenges due to differences in image perspectives and modalities, which make image matching and localization difficult. In target detection, distant objects often appear small and indistinct against complex backgrounds, leading to missed or delayed detection. This talk will present a multi-source information fusion method for intelligent matching of cross-modal images from different perspectives, aiming to improve the accuracy of UAV visual navigation and localization. It will also introduce a multi-feature fusion approach for detecting weak and small targets, achieving fast and reliable recognition of distant objects in complex environments.
Bio: Zhunga Liu is a Professor at School of Automation, Northwestern Polytechnical University (NPU). His current research interests focus on intelligent information fusion, target recognition and tracking, and navigation and guidance. He has published some papers in international journals such as IEEE TPAMI, IEEE TGRS, and IEEE TAES, and has led several major projects, including key programs of the National Natural Science Foundation of China. Prof. Liu has received the First Prize of Natural Science of Shaanxi Province, the First Prize of Natural Science from the Chinese Association of Automation, and the Youth Science and Technology Award from the Chinese Society of Aeronautics and Astronautics. He also serves on the editorial boards of IJAR, Science China Information Sciences, and Acta Aeronautica et Astronautica Sinica, and is a Board Member of the Chinese Society of Aeronautics and Astronautics.
Prof. Min Liu, Hunan University, China
Speech Title: Intelligent Surgical Robot Multimodal Perception
Abstract： The breakthrough and comprehensive intelligent transformation and upgrading of core technologies in high-end medical equipment such as surgical robots is a major national strategic task aimed at the forefront of world technology, major national needs, and people's lives and health. It provides decisive guarantee and strong support for breaking the technological monopoly of high-end digital medical equipment in Europe and America. The existing surgical robots lack an effective multimodal surgical target collaborative perception system, which seriously restricts their promotion and application in emergency response to major national emergencies such as national defense security and epidemic disasters. In response to the challenging issues mentioned above, this lecture provides an in-depth introduction to the basic principles and key methods of multimodal perception of surgical robots from preoperative, intraoperative to postoperative, and showcases some of the progress our team has made so far, providing important guarantees for reducing medical accidents in China.
Bio: Min Liu, a secondary professor at Hunan University and Party Committee Secretary of the School of Artificial Intelligence and Robotics, is a recipient of the National Science Fund for Distinguished Young Scholars and a Youth Yangtze River Scholar of the Ministry of Education. She serves as the lead scientist for the National Key R&D Program and a core member of the Innovative Research Group under the National Natural Science Foundation of China. Holding a bachelor's degree from Peking University and a Ph.D. from the University of California, Riverside, she is Vice Chairman of the Hunan Automation Society, Director of the Key Laboratory of Advanced Manufacturing Vision Inspection and Control Technology in the Machinery Industry, and Deputy Director of the Youth Working Committee of the China Image Graphics Association. He has led two National Key R&D Program projects and one key project under the National Natural Science Foundation of China. As the first or corresponding author, she has published over 50 papers in IEEE transactions and received five provincial and ministerial-level scientific awards.
Prof. Xiaosheng Si, PLA Rocket Force University of Engineering, China
Speech Title: Direct Method of Reverse Modeling for Remaining Life Prediction of Stochastic Degradation Systems
Abstract: Residual life prediction is a key technology for ensuring the long-term safe and reliable operation of major equipment, receiving widespread attention across multidisciplinary fields such as automation and mechanical manufacturing, with significant application prospects. However, the primary challenge has long been how to transform the degradation process into residual life through failure criteria in the commonly used indirect method to achieve the solution of residual life distribution. Specifically, in the residual life prediction of major equipment with typical characteristics like complex nonlinearity, the process of solving residual life distribution in the indirect method is difficult or even impossible to achieve accurately and efficiently, necessitating reliance on various model simplification or approximation techniques. Precisely because of this, residual life prediction for major equipment under conditions such as complex nonlinearity and multivariate coupling has remained a persistent pain point in the field that has yet to be effectively resolved. Against this backdrop, this report primarily introduces the "Inverse Modeling Direct Method for Residual Life Prediction" recently proposed by the research team and preliminary research findings. Its core academic concept is: constructing an inverse mapping model from equipment degradation state to service time, revealing the evolution pattern of service time with degradation state, and thereby inputting the equipment failure criteria into the established inverse mapping model to directly obtain the residual life distribution, eliminating the need for the conversion steps in the indirect method.
Bio: Xiaosheng Si received the B.Eng., M.Eng., and Ph.D. degrees in control science and engineering from the Department of Automation, PLA Rocket Force University of Engineering, Xi’an, China, in 2006, 2009, and 2014, respectively.
He is currently a Professor with the PLA Rocket Force University of Engineering. He has authored or coauthored more than 50 articles in several journals, including European Journal of Operational Research, IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, IEEE TRANSACTIONS ON RELIABILITY, IEEE TRANSACTIONS ON FUZZY SYSTEMS, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART A, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, Reliability Engineering and System Safety, and Mechanical Systems and Signal Processing. His research interests include evidence theory, expert systems, prognostics and health management, reliability estimation, predictive maintenance, and lifetime estimation. Dr. Si is an Editorial Member of Mechanical Systems and Signal Processing, and ASME/IEEE T Mech. He is an active reviewer of a number of international journals.