Youmin Zhang (IEEE Fellow, CSME Fellow)
Concordia University, Canada
BIO: Dr. Youmin Zhang is currently a Professor in the Department of Mechanical, Industrial, and Aerospace Engineering at Concordia University, Canada. His research interests include monitoring, diagnosis, and physical fault/cyber-attack tolerant/resilient control, as well as the guidance, navigation, and control of unmanned systems (air, space, land, marine, and autonomous vehicles, etc.) and smart grids. These research areas have applications in solving safety and security issues of forest fires, smart grids, and smart cities within the framework of cyber-physical systems by combining remote sensing and image processing techniques. He has published 10 books and over 600 journal and conference papers, which have been cited up to 31,348 times on Google Scholar, with the h-index of 87, and i10-index of 431 currently. His publications have been ranked #1 worldwide in "Aircraft Systems" and "Fault Tolerance" (lifetime) and #2 worldwide in "Unmanned Aerial Vehicles" (lifetime), making him the only contributor from Canada in that category, according to the recent ranking by ScholarGPS. He has also been continuously recognized in the "World's Top 2% Scientists" by Stanford University's ranking since its launch in 2021.
Prof. Zhang is a Fellow of IEEE and CSME (Canadian Society of Mechanical Engineering), and served as the President of the International Society of Intelligent Unmanned Systems from 2019 to 2022. He has held roles as (Deputy) Editor-in-Chief and (Advisory) Editorial Board Member for more than 20 journals. Additionally, he has served as (Honorary) General Chair and Program Chair for several conferences related to autonomous/unmanned systems, renewable energies, and smart cities. More detailed information can be found at http://users.encs.concordia.ca/~ymzhang/.
Shugen Ma (IEEE Fellow, AAIA Fellow, JSME Fellow)
Hong Kong University of Science and Technology (Guangzhou), China
BIO: Professor Shugen Ma (IEEE Fellow, AAIA Fellow, JSME Fellow, Foreign Fellow of the EAJ) received a Ph.D. in Mechanical Engineering Science from the Tokyo Institute of Technology. He is now a professor of the Robotics and Autonomous Systems Thrust of Systems Hub at the Hong Kong University of Science and Technology (Guangzhou). Before he joined HKUST (GZ), he was a professor at Ritsumeikan University and a visiting professor at Johns Hopkins University, Shenyang Institute of Automation of CAS, Tianjin University, and Harbin Institute of Technology, respectively. Professor Ma’s research interests include but are not limited to the design and control of environment-adaptive robots, amphibious robots, field robotics, and biologically inspired robotics. He has published over 500 papers in major journals and conference proceedings, developed over 50 novel robot systems with over 80 patents, and supervised 45 doctoral students and over 100 M. Phil. students to graduation. He has also been featured in the list of the world’s top 2% of scientists published by Stanford University.
Prof. Ma is a Professor Emeritus of Ritsumeikan University, a Foreign Fellow of the Engineering Academy of Japan, and the general chair of IROS 2022 (a top robotics conference). He founded the ROBIO conference in 2004, was the general chair of ROBIO 2004, ROBIO 2010, and ROBIO 2016, and served many societies and conferences.
Angelo Cangelosi
University of Manchester, UK
BIO: Angelo Cangelosi is Professor of Machine Learning and Robotics at the University of Manchester (UK) and co-director and founder of the Manchester Centre for Robotics and AI. He was selected for the award of the European Research Council (ERC) Advanced grant (funded by UKRI). His research interests are in cognitive and developmental robotics, neural networks, language grounding, human robot-interaction and trust, and robot companions for health and social care. Overall, he has secured over £40m of research grants as coordinator/PI, including the ERC Advanced eTALK, the UKRI TAS Trust Node and CRADLE Prosperity, the US AFRL project THRIVE++, and numerous Horizon and MSCAs grants. Cangelosi has produced more than 400 scientific publications. He is Editor-in-Chief of the journals Interaction Studies and IET Cognitive Computation and Systems, and in 2015 was Editor-in-Chief of IEEE Transactions on Autonomous Development. He has chaired numerous international conferences, including ICANN2022 Bristol, and ICDL2021 Beijing. His book “Developmental Robotics: From Babies to Robots” (MIT Press) was published in January 2015, and translated in Chinese and Japanese. His latest book “Cognitive Robotics” (MIT Press), coedited with Minoru Asada, was recently published in 2022 (Chinese translation in 2025).
Speech Title: The Importance of Starting Small with Baby Robots: Developmental Robotics for Language Grounding
Abstract: Cognitive developmental robotics aims to develop robots capable of human-like learning, interaction, and behavior by grounding concrete and abstract concepts in sensorimotor experiences and social interactions. This talk introduced examples on language grounding in cognitive developmental robotics, and explores how principles like “starting small”, “embodied intelligence” and “super-embodiment” can address the limitations of AI tools, such as large language models (LLMs), which rely heavily on large datasets and lack sensorimotor grounding. By integrating incremental, multimodal learning and redefining embodiment to encompass physical, mental, and social processes, we can enable robots to better understand and utilize abstract concepts. The talk will also reflect on the pros and cons of using foundation models in cognitive robotics.
Maria Pia Fanti (IEEE Fellow)
Polytechnic University of Bari, Italy
BIO: Maria Pia Fanti received the Laurea degree in electronic engineering from the University of Pisa, Pisa, Italy. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983, she has been with the Department of Electrical and Information Engineering of the Polytechnic University of Bari, Italy, where she is currently a Full Professor of system and control engineering and Chair of the Laboratory of Automation and Control. Her research interests include management and modeling of complex systems, such as transportation, logistics and manufacturing systems; discrete event systems; Petri nets; consensus protocols; fault detection. Prof. Fanti is IEEE fellow and has published more than 320 papers and two textbooks on her research topics.
She was senior editor of the IEEE Trans. on Automation Science and Engineering and she is Associate Editor of the IEEE Trans. on Systems, Man, and Cybernetics: Systems. She was member at large of the Board of Governors of the IEEE Systems, Man, and Cybernetics Society, and currently she is member of the AdCom of the IEEE Robotics and Automaton Society, and chair of the Technical Committee on Automation in Logistics of the IEEE Robotics and Automation Society. Prof. Fanti was General Chair of the 2011 IEEE Conference on Automation Science and Engineering, the 2017 IEEE International Conference on Service Operations and logistics, and Informatics and the 2019 IEEE Systems, Man, and Cybernetics Conference.
Ching-Chih Tsai (IEEE Fellow)
National Chung Hsing University, Taiwan
BIO: Ching-Chih Tsai received the Diploma degree in electrical engineering from the National Taipei Institute of Technology, Taipei, Taiwan, in 1981, the M.S. degree in control engineering from National Chiao Tung University, Hsinchu, Taiwan, in 1986, and the Ph.D. degree in electrical engineering from Northwestern University, Evanston, IL, USA, in 1991. He is currently a Life Distinguished Professor at the Department of Electrical Engineering, National Chung Hsing University (NCHU), Taichung. He has been elevated as the Fellow of Institute of Electrical and Electronic Engineering (IEEE), Institute of Engineering Technology (IET), Chinese Automatic Control Society (CACS), Robotics Society of Taiwan (RST), and Taiwan Fuzzy Systems Association (TFSA). From 2003 to 2005, he served as the Director of the Center of Research Development and Engineering Technology, College of Engineering, National Chung Hsing University. In 2006, he served as the Director of the Center for Advanced Industry Technology and Precision, National Chung Hsing University. He served as the department chair of the Department of Electrical Engineering, NCHU, from 2012 to 2014. From 2012 to 2016, he served two-term President of the Chinese Automatic Control Society (CACS), Taiwan. From 2016 to 2019, he served two-term President of the Robotics Society of Taiwan (RST). From 2019 to 2023, he served as the President Elect and President for International Fuzzy Systems Association (IFSA), respectively. Since August, 2024, he has served as the Dean of College of Electrical Engineering and Computer Science. He also served as two-term BoG members of IEEE SMCS from 2017 to 2019 and from 2022 to 2024, respectively. In recent years, he has served associate editors of IEEE Transactions on Systems, Man Cybernetics: Systems, IEEE Transactions on Industrial CPS, and International Journal of Fuzzy Systems, respectively. He has published and co-authored more than 700 technical articles. His current research interests include intelligent control systems, smart mobile robotics and intelligent automation with their applications to smart hospitals, semiconductor manufacturing and clean energy.
Speech Title: Technology Development and Field Verifications of AI-Based Nursing Robots in a Smart Hospital
Abstract: To overcome the six technical pain points caused by medical nursing and material handling robots currently faced by smart hospitals, this talk is aimed to design and implemented smart nursing robots some breakthrough techniques by integrating discriminative AI technologies (including deep learning, broad learning, reinforcement learning), generative AI (GAI), intelligent control, embedded control, autonomous mobile robots (AMR) and AI chip design technologies. These new smart nursing robots have generative-AI robot control frameworks and multi-modal natural human-computer interaction, and even have some breakthrough techniques and methodologies such as more advanced V-SLAM chip designs with hardware accelerators, GAI autonomous navigation and task generation and execution, reliable smart conversation or dialogue, AI visual environment perception, intelligent multi-robot assignment and scheduling, digital twin with robot task planning, in order to achieve medical nursing and equipment handling tasks in the cluttered, dynamic and crowded smart hospital space. This talk will focus on how to deeply develop these breakthrough technologies for medical nursing robots and how to proceed with the robots’ field verifications in the Chang Chi Hospital, Changhua, Taiwan, in order to better address the practical issues of smart hospitals. Some experimental results will be companied with the proposed methods together with the built robot.
David Banjerdpongchai
Chulalongkorn University, Thailand
BIO: David Banjerdpongchai received B.Eng. degree (First class honors) from Chulalongkorn University, and M.S. and Ph.D. degrees from Stanford University, all in Electrical Engineering, respectively. He has been with the department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University. Currently, he is a professor of Electrical Engineering and the head of the Center of Excellence in Intelligent Control Automation of Process Systems. He is a senior member of IEEE, President of ECTI Association (2024-2025), and a founding chair of IEEE Control Systems Society Thailand Chapter (2015-2021). He served as a general co-chair of ECTI-CON 2013, ICA-SYMP 2019, ECTI-CON 2024, ISCIT 2024, ECTI-CON 2025, SICE FES 2025, associate editor of IJCAS and a section editor-in-chief of ASEAN Engineering Journal. His research interests are energy management systems, control design of nonlinear systems, and convex optimization in robust control problems.
Speech Title: Robust Iterative Learning Control for Flexible Link System
Abstract: This research proposes a convex optimization design of robust iterative learning control (ILC) algorithm for linear systems subject to parametric uncertainties. The system model is described by the Markov matrix as an affine function of parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. We reformulate the design problem as a convex optimization over linear matrix inequalities (LMIs). The LMI-based design problem can efficiently solved using available convex optimization software. The robust ILC algorithm has been developed, and the convergence of the control input and the error can be guaranteed. We conduct a computer simulation of the robust ILC algorithm with a flexible link model and verify with the implementation on a real flexible link system. The objective of control for the flexible link is to iteratively track the desired reference angle under the parameter variation such as the change of load mass. The experiment results are close to the simulation results. Thus, the robust ILC algorithm can be effectively applied to the flexible link system.