Hong ZHANG
Southern University of Science and Technology, China

Biography: Dr. ZHANG Hong is currently a Chair Professor in the Department of Electronic and Electrical Engineering at Southern University of Science and Technology (SUSTech) where he directs “Shenzhen Key Laboratory on Robotics and Computer Vision”.  His research interests include robotics, autonomous vehicles, computer vision, and image processing. Prior to joining SUSTech, he was a Professor in the Department of Computing Science, University of Alberta, Canada, where he worked for over 30 years.  He held an NSERC Industrial Research Chair from 2003-2017, and made significant contributions in robotics research. He served as the Editor-in-Chief of IROS Conference Paper Review Board, a flagship conference of the IEEE Robotics and Automation Society (RAS), from 2020-2022.  He is currently serving a three-year term as a member of the Administrative Committee (AdCom) of IEEE RAS (2023-2025).  In recognition of his many contributions, Dr. Zhang was elected a Fellow of IEEE and a Fellow of the Canadian Academy of Engineering.

Speech Title: Applications of Foundation Models in Robotics

Abstract: Development in artificial intelligence (AI) has always opened doors to its robotics applications. Most recently, the emergence of foundation models such as large language models, vision language models, large vision models, etc. has led to solutions to existing challenges in robot navigation and manipulation. Foundation models in these cases serve as a rich source of prior information for robot sensory perception and decision making that is difficult or impossible to obtain otherwise. This presentation first quickly sumarizes representative state-of-the-art foundation models. It is then followed by a description of some research projects in the Shenzhen Key Laboratory on Robotics and Computer Vision at SUSTech on (a) robot grasping and manipulation (b) mobile robot object search and (c) embodied AI.

Peng SHI
The University of Adelaide, Australia

Biography: Peng Shi received the PhD degree in Electrical Engineering from the University of Newcastle, Australia, and the PhD degree in Mathematics from the University of South Australia. He was awarded the Doctor of Science degree from University of Glamorgan, Wales; and the Doctor of Engineering degree from the University of Adelaide, Australia. He is now a Distinguished Professor, Director of Laboratory of Advanced Unmanned Systems, and Laboratory of Cyber-Physical Human Systems at University of Adelaide, Australia. His research interests include systems and control theory and applications to autonomous and robotic systems, intelligence systems, and cyber-physical systems. He has published widely in those areas. He received many recognitions and awards, such as the IEEE Nobert Wiener Award from IEEE Systems, Man and Cybernetics (SMC) Society in 2024, the Ramesh Agarwal Life-time Achievement Award from the International Engineering and Technology Institute (IETI) in 2023, the Meritorious Service Award from IEEE SMC Society in 2023, the M. A. Sargent Medal Award from the Institution of Engineers Australia (IEAust) in 2022, the Highly-Cited Researcher from Thomson Reuters/Clarivate Analytics since 2014, and Field Leader from The Australian Research Review from 2019-2023, the Outstanding Research Achievement Award from University of Adelaide in 2020, the Chancellor’s Gold Medal Award for Outstanding Research Performance from Victoria University in 2018, the Runner-up of Scopus Researcher of the Year Award (Elsevier, 2017), and the Andrew Sage Best Transactions Paper Award from IEEE SMC Society in 2016.  His professional service includes a member of Board of Governors, Vice President of IEEE SMC Society, the President of the International Academy of Systems and Cybernetic Science (IASCYS), and Distinguished Lecturer of IEEE SMC Society. He is a member of the Academy of Europe, the European Academy of Science and Arts, the Academy of Romanian Scientists, and an Academician of IASCYS. He is a Fellow of IEEE, IET, IETI, IEAust, and CAA. He serves/served on the editorial board of journals such as Automatica, IEEE Transactions on (Automatic Control, Fuzzy Systems, Circuits and Systems, Artificial Intelligence). Currently he serves as the Editor-in-Chief of IEEE Transactions on Cybernetics, a Senior Editor of IEEE Access, and the Co-Editor-in-Chief of Australian Journal of Electrical and Electronics Engineering.

Speech Title: Consensus and Formation Control for Multi-agent Systems

Abstract: The key features of Multi-agent Systems (MAS) are communication, coordination, and collaboration, by which the agents can achieve a common (and possibly difficult) goal in a more effective and efficient way. Three main topics within the realm of MAS are consensus, flocking and formation control. Cooperating processes often require agents to reach a consensus, which is the fundamental problem in MAS. Flocking (or swarming) is a self-organizing behavior originated from small-size animals with lower intelligence, which enables the emergence of swarm intelligence to improve the whole system survivability and competitiveness. Formation control generally aims to drive the agents to achieve a desired formation, scalable and/or changeable. In this talk, modeling analysis and design of a variety of distributed schemes for consensus and formation control are introduced. Simulations and experimental examples are provided to demonstrate the potential of the proposed new design techniques.      

Shen Yin
Norwegian University of Science and Technology, Norway

Biography:Shen Yin is the DNV Endowed Professor in the Department of Mechanical and Industrial Engineering at the Norwegian University of Science and Technology, Norway. He earned his Dr.-Ing. and MSc degrees from the University of Duisburg-Essen, Germany. His research interests include fault diagnosis, prognosis, and fault-tolerance strategies; machine learning and data-driven approaches for reliability and safety; system and control theory. He has been elected as an IEEE Fellow and a member of the Norwegian Academy of Technological Sciences (NTVA).

Speech Title: Predictive Insights: Potential Solutions for Remaining Useful Life Estimation

Abstract: This talk introduces approaches for estimating the Remaining Useful Life (RUL) of technical systems, crucial for optimizing maintenance strategies and enhancing reliability. Initially, it reviews traditional model-based techniques, such as mechanistic models for predicting the longevity of batteries through an understanding of component physics. When these approaches prove inadequate, the focus shifts to advanced data-driven and machine learning (ML) methods, which facilitate RUL estimation without extensive domain knowledge. The presentation highlights three main challenges: managing limited data, enhancing model robustness and sensitivity, and integrating diverse data sources for better insights.

Guilin Yang
Ningbo Institute of Materials Technology and Engineering
Chinese Academy of Sciences, Ningbo, China

Biography: Guilin YANG is currently a professor, a PhD supervisor, and the deputy president of Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences. He received his B. Eng degree and M. Eng degree from Jilin University in 1985 and 1988 respectively, and Ph.D. degree from Nanyang Technological University in 1999, all in Mechanical Engineering. From 1998 to 2013, he was with the Singapore Institute of Manufacturing Technology, Singapore, as a scientist, a senior scientist, and then the manager of the Mechatronics Group. He has long been engaged in the research areas of precision mechatronics, industrial robotics, and manufacturing automation. He has been the principal investigators for a number of major research projects in precision actuators, modular robots, parallel robots, cable-driven robots, and industrial robot applications. He has published over 300 technical papers in referred journals and conferences, authored two books, and filed 100 more patents. He received the R&D 100 Award in 2014 and the golden award of “Good Design” of China in 2020. He has also served as associate editors and guest editors for a number of scientific journals.

Speech Title: Advanced Industrial Robotics for Intelligent Manufacturing

Abstract: As a pivotal enabling technology for intelligent manufacturing, industrial robotics has undergone rapid advancement in recent years, dramatically expanding its range of applications. This talk will focus on research advances in industrial robot application technologies and new-generation industrial robots, along with their implementations in intelligent manufacturing. Regarding industrial robot application technologies, key aspects including rapid teaching and programming, calibration and error compensation, force-controlled end-effectors as well as hybrid force-motion control techniques will be introduced. For new-generation industrial robots, emphasis will be placed on the core components, such as torque motors, gearboxes, and joint modules, of lightweight collaborative robots and mobile manipulators, along with system integration，compliance motion control, and mobile manipulation technologies. These research works hold significant implications for performance optimization, function enhancement, and widespread adoption of industrial robots.

 

Dongrui Wu
Huazhong University of Science and Technology, China

Biography: Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. 
Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (11000+ Google Scholar citations; h=54). He received the IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics (SMC) Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation Early Career Award in 2021, and the Ministry of Education Young Scientist Award in 2022. His team won the First Prize of the China Brain-Computer Interface Competition in four successive years (2019-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

Speech Title: Machine Learning in Brain-Computer Interfaces

Abstract: A brain-computer interface (BCI) enables direct communication between the brain and external devices. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG decoding. Additionally, adversarial security and privacy protection are also very important to the broad applications of BCIs. This talk will introduce machine learning algorithms for accurate, secure and privacy-preserving BCIs.