Prof. Mitsuhiro Hayashibe
(Keynote Speaker)

Tohoku University, Japan

Keynote Lecture: Motor Synergy Emergence in Redundancy and Synergistic Whole-body Motion Generation through Deep Reinforcement Learning

Abstract: In recent studies, Deep Reinforcement Learning (DRL) has shown promising results for quadruped robots. However, training humanoids with full degrees of freedom remains a significant challenge for the very high computational cost. The high-dimensional action space complicates the optimization process for generating expected movement patterns, particularly for different stylized motions and for different motion frequencies. Therefore, we propose an AI-CPG algorithm, which integrates central pattern generators with DRL and facilitates the learning of complex movement patterns without intricate reward function designs. The point of this proposed study is to connect reinforcement learning and imitation learning through the bio-inspired structure of CPG (Central Pattern Generators).

In addition, neuroscientific study on how motor synergy emerge in redundancy during learning process is also introduced to understand why humans use low-dimensional synergy structure for energy efficiency. Humans exploit motor synergies for motor control; however, how they emerge during motor learning is not clearly understood. Few studies have dealt with the computational mechanism for generating synergies in whole-body dimensionality. Interestingly, increasing the weight of symmetry loss resulted in increased energy efficiency and synergetic motion patterns concurrently. This suggests that locomotor synergies can emerge through learning processes, complementing the understanding of synergy emergence mechanisms.

Biography: Dr. Mitsuhiro Hayashibe is a Professor at the Department of Robotics, Graduate School of Engineering, Tohoku University, Japan, and founder of the Neuro-Robotics Lab since 2017. He is concurrently with the Graduate School of Biomedical Engineering, Tohoku University. He obtained PhD at University of Tokyo in 2005, and the Habilitation degree at University of Montpellier in 2015. He was previously an Assistant Professor with the Department of Medicine, Tokyo Jikei University School of Medicine (東京慈恵会医科大学医学部) for 2001-2006, and a Tenured Research Scientist with INRIA (Institut National de Recherche en Informatique et en Automatique, フランス国立情報学自動制御研究所) and University of Montpellier, France for 2008-2017. He has been a visiting researcher at RIKEN Center for Brain Science and TOYOTA Collaboration Center (理化学研究所 脳神経科学研究センター) since 2012, also at EPFL (École Polytechnique Fédérale de Lausanne, スイス連邦工科大学ローザンヌ校) for 2016 with Swiss National Science Foundation grant. He is co-chair of IEEE Robotics and Automation Society Technical Committee on Human Movement Understanding. He was awarded with the 15th Annual Delsys Prize 2017 for Innovation in Electromyography from De Luca Foundation, USA.

Prof. Hongliang REN
(Keynote Speaker)

The Chinese University of Hong Kong (CUHK), Hong Kong, China

Keynote Lecture: Compliant surgical motion generation and perception towards intelligent minimally invasive robotic procedures

Abstract: Minimally Invasive Surgeries (MIS) emerging in modern medical treatment have brought new opportunities and challenges for procedure-specific surgical motion generation and the associated motion understanding, which are the foundation of intelligent robotic manipulation and guiding interventions. Image-guided robotic surgery is expected to increase the precision, flexibility, and repeatability of surgical procedures but poses challenges for system development. This talk will highlight our recent developments in dexterous robotic motion generation with motion perception towards intelligent image-guided minimally invasive procedures. The procedure-specific telerobotic surgical systems can assist surgeons in performing dexterous manipulations using continuum motion generation mechanisms with variable stiffness and context awareness.

Biography: Prof. Hongliang Ren received his Ph.D. in Electronic Engineering (Specialized in Biomedical Engineering) from The Chinese University of Hong Kong (CUHK) in 2008. He has served as an Associate Editor for IEEE Transactions on Automation Science & Engineering (T-ASE) and Medical & Biological Engineering & Computing (MBEC). He has navigated his academic journey through Chinese University of Hong Kong, Johns Hopkins University, Children’s Hospital Boston, Harvard Medical School, Children’s National Medical Center, United States, and National University of Singapore (NUS).

His areas of interest include biorobotics, intelligent control, medical mechatronics, soft continuum robots, soft sensors, and multisensory learning in medical robotics. He is the recipient of CUHK Young Researcher Award, NUS Young Investigator Award and Engineering Young Researcher Award, IAMBE Early Career Award 2018, Interstellar Early Career Investigator Award 2018, ICBHI Young Investigator Award 2019, and Health Longevity Catalyst Award 2022 by NAM & RGC, Best Paper Awards in IEEE-ROBIO (2019 & 2013), IEEE-RCAR2016, IEEE-CCECE2015, IEEE-Cyber2014 among 30+ others awards.

He has been constantly listed among the world’s top 2% of the most-cited scientists by Stanford University in the career-long category.

He frequently served as an expert reviewer/judge for international funding agencies (60+ proposal reviews) of 10+ countries/regions (including Switzerland, Belgium, UK, Kazakhstan, Poland, Hong Kong, Macau, Chilean, China, Singapore etc.)

Prof. David Banjerdpongchai
(Keynote Speaker)

Chulalongkorn University, Thailand

Keynote Lecture: Design of Supervisory Model Predictive Control for HVAC Systems

Abstract: This talk presents recent trends in energy intensity and energy demand in ASEAN and addresses key energy policies as part of Thailand 4.0. Electricity consumption has been increasing significantly due to the earth’s rising global temperatures, leading to higher energy usage in Heat, Ventilation and Air Condition (HVAC) systems. Choosing the conventional setpoint temperature could reduce unnecessary power consumption, thus leading to cost savings. This talk presents the design of Supervisory Model Predictive Control (SMPC) for HVAC systems with multiple zones. The design aims to shave the peak demand and maintain occupants’ thermal comfort. Two methods of SMPC are developed, namely, centralized SMPC and decentralized SMPC. We apply the sparse Quadratic Programming (QP) solver using the interior point method. The results indicate that centralized supervisory control yields better outcomes, as demonstrated by a trade-off curve between total operating costs and thermal comfort. Moreover, centralized model predictive control successfully achieved satisfactory results in both tracking the reference signal and optimizing power consumption. Utilizing the sparse QP solver can yield faster computation compared to the standard QP solver, making it more suitable for the design of SMPC.

Biography: David Banjerdpongchai has been with the department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University. Currently, he is a professor of Electrical Engineering, head of Center of Excellence in Intelligent Control Automation of Process Systems, and deputy director of International School of Engineering. He is a senior member of IEEE, a founding chair of IEEE Control Systems Society Thailand Chapter, Vice President of ECTI Association (2022-2023), President of ECTI Association (2024-2025), an executive board member of ECTI Association, and a chair of IEC TC65 Thailand National Committee. In the past, he served as a chair of Systems and Control Technical Committee of ECTI Association and a member of Steering Committee of Asian Control Association. He served as General Co-chair of ECTI-CON 2013, ICA-SYMP 2019, ECTI-CON 2024, ECTI-CON 2025, SICE Fes 2025, and TPC chair of ECTI-CON 2014 and TPC Co-chair of SICE 2020. He has served as an Associate Editor for IJCAS and a Section Editor-in-Chief for ASEAN Engineering Journal. He has published over 200 articles in journal and conference proceedings and a textbook on Dynamical Control Systems. His research interests are energy management systems, advanced process control, iterative learning control, and robust control applications.

Prof. Lixuan Lu
(Keynote Speaker)

Ontario Tech University, Canada

Keynote Lecture: An Academic Perspective of Challenges and Opportunities of Instrumentation and Control for Nuclear Power Generation

Abstract: TBA

Biography: Lixuan Lu obtained her Ph.D degree in Electrical and Computer Engineering from the University of Western Ontario (now Western University), Canada in 2005. She joined the University of Ontario Institute of Technology (now Ontario Tech University), Canada as an assistant professor in 2005, and obtained her associate professorship and full professorship in 2011 and 2017, respectively. Her research interests cover dynamic probabilistic safety assessment, reliability analysis methods, networked control systems in safety-critical applications, passive safety systems, risk-informed design, risk-informed maintenance, and small modular reactors.

Prof. Siti Anom Ahmad
(Keynote Speaker)

Universiti Putra Malaysia, Malaysia

Keynote Lecture: AI for Healthy Ageing: Automated Interventions Based on Physiological and Environmental Signals

Abstract: As the world’s population continues to age, helping older adults live healthily and independently is becoming more important. With recent advances in artificial intelligence (AI), wearable sensors, and smart monitoring systems, we now have new ways to support ageing care. This keynote will explore how AI can analyze signals from the body—like heart rate, muscle movement, and balance—to detect early signs of health decline or risk of falls.

In addition to body signals, environmental factors such as air quality and temperature also affect the health of older people. By combining data from both the body and the environment, we can build real-time systems that take action automatically—such as sending alerts to caregivers or switching on ventilation systems.

Based on research in biomedical engineering, intelligent control systems, and ageing technology (gerontechnology), this talk will present real-world examples and challenges in creating smart systems for elderly care. These technologies have the potential to reduce healthcare costs, offer personalized support, and help older adults stay safe and independent in their own homes.

Biography: Siti Anom Ahmad is a Professor at the Faculty of Engineering, Universiti Putra Malaysia. Siti Anom received her PhD in Electronics in 2009 and MSc in Microelectronics System Design in 2004 from the University of Southampton, UK. She received her BEng in Electronic/ Computer from UPM in 1999. She is a Professional Engineer of Board of Engineers Malaysia, Chartered Engineer of Institute of Engineering and Technology (IET), Senior Member of Institute of Electrical & Electronic Engineering (IEEE) and a member of the Institute of Engineers Malaysia (IEM). Her research interests are biomedical engineering, artificial intelligence, gerontechnology, and intelligent control systems.

Prof. Giuseppe Carbone
(Keynote Speaker)

University of Calabria, Italy

Keynote Lecture: Cost-Oriented Innovation in Robotic Grippers and Hands: Design Strategies for Accessible Automation

Abstract: This presentation unveils pragmatic approaches to designing robotic grippers and hands that deliver solid performance without hefty price tags. By embracing simplicity in mechanism design and modular architectures, developers can reduce production and maintenance costs while retaining essential functionality. We’ll discuss streamlined actuation methods—leveraging fewer motors and clever linkages—to achieve reliable operation in everyday tasks. Attention will turn to integrating basic sensing capabilities, such as tactile and proximity feedback, to enhance adaptability without driving up expenses. Through illustrative case examples and simulation snapshots, it demonstrates how iterative design and virtual testing can pinpoint cost-saving opportunities early on.

Biography: Giuseppe Carbone has got his PhD degree in Robotics from the University of Cassino, Italy, in 2004 He has been visiting professor at Universidad Carlos III of Madrid, Beihang University, Waseda University, and several other well-reputed International Research Institutions. From 2024 he has joined East China Jiaotong University. From 2020 he is Chair of IFToMM TC on Robotics and Mechatronics. From 2018 he has joined University of Calabria, Italy. From 2018 to 2021 he has been Visiting professor at Sheffield Hallam University, UK where he served as Senior Lecturer and member of the Executive board of Sheffield Robotics from 2015 to 2017. He has been Scientific Director of the International Research Laboratory Intelligent Robotic Systems and Technologies. Among others he is Editor-in-Chief of Robotica Journal (Cambridge Univ. Press), Section EIC of Journal of Bionic Engineering, MDPI Robotics, MDPI Machines, Technical Editor of IEEE/ASME Transactions on Mechatronics. He has been PI or co-PI of more than 20 projects including 7th European Framework and H2020 funds. He has received more than 20 Best Paper awards and more than 10 International Best Patent awards. His research interests cover aspects of Engineering Design, Mechanics of Robots, Mechanics of Manipulation and Grasp, Mechanics of Machinery with over 500 research paper outputs, 20 patents, and 16 Phd completions (8 ongoing). He has been also member of 20 PhD evaluation Commissions in Italy, Spain, Finland, UK, Romania, Mexico, India. He has been invited to deliver Keynote speeches and lectures on his research activity at more than 30 International events. He edited/co-edited four books that have been published by Springer and Elsevier International Publishers. h-index 40 n. citations >6000 (source google scholar). In January 2023 he received an Honoris Causa Doctoral Degree from Technical University of Cluj-Napoca (Romania). In June 2023 he received an Honoris Causa Doctoral Degree from University of Craiova (Romania).

Prof. Jiaxiang Luo
(Keynote Speaker)

South China University of Technology, China

Keynote Lecture: Key Technologies for Industrial Image Anomaly Detection and Defect Recognition

Abstract: With the increasing complexity of visual perception tasks in advanced manufacturing, industrial image anomaly detection and defect recognition are shifting from traditional rule-based methods to intelligent learning models with strong generalization capabilities. This report presents the latest research advances from our laboratory aimed at improving the accuracy and robustness of anomaly and defect detection in industrial scenarios. The key contributions include: a self-supervised framework for texture-structure anomaly detection, an anomaly detection method combining knowledge distillation and image reconstruction, a noise-robust training strategy based on collaborative adversarial flows, a multimodal industrial anomaly detection approach integrating single- and cross-modal features, a decoupled two-stage framework for defect object detection, and a reference-image-guided method for industrial defect localization. These methods have been validated on multiple real-world industrial datasets, demonstrating strong detection performance, engineering applicability, and scalability, thereby offering new perspectives and technical support for intelligent visual inspection in manufacturing.

Biography: Professor Luo Jiaxiang is a doctoral supervisor at the School of Automation Science and Engineering, South China University of Technology. Her long-term research focuses on intelligent perception and autonomous decision-making, encompassing theoretical approaches such as data analysis and machine learning, deep learning and autonomous learning, and computational intelligence. Her work also involves intelligent perception technologies like image understanding and multimodal sensing, alongside intelligent operation technologies such as autonomous robot learning and motion planning.

In these research areas, she has led over 20 national, provincial, and ministerial-level projects, published more than 70 SCI and EI-indexed papers, and been granted over 20 invention patents. She was awarded the Second Prize of the Guangdong Provincial Science and Technology Progress Award.

Prof. Jingjing Ji
(Keynote Speaker)

Huazhong University of Science and Technology, China

Keynote Lecture: AI for Fly-by-Feel Flight Measurement and Control

Abstract: Accurate determination of aerodynamic loads is a prerequisite for autonomous control of morphing aircraft to achieve high adaptability. Smart aircraft skin technology based on flexible ultra-thin sensors provides a new solution for in-situ measurement of non-perturbed aerodynamic characteristics, but it can only obtain limited discrete data on the surface of the aircraft. How to obtain the structural deformation and aerodynamic distribution characteristics based on the limited number of measurement points is the research frontier. This talk presents results of our research studies on AI-enhanced field reconstruction for aerodynamic load monitoring. The talk will begin with a morphing wing deformation monitoring, where the displacement field is reconstructed from several strain measurements in real-time. Next, the talk will present the aerodynamic sensing capability of flexible skins where the spatial fluid field is reconstructed from the limited pressure measurement points on the wing surface. A Bayesian-based data assimilation approach that fuses computational fluid dynamics and experimental fluid dynamics, while a data-driven approach is also attempted for aerodynamic perception. In conclusion, the in-situ aerodynamic field measurement technology via flexible smart skins will contribute to the rapid development of unmanned aircraft intelligence.

Biography: Jingjing Ji received the B.S. and Ph.D. degrees in mechanical engineering from Zhejiang University, Hangzhou, China, in 2008 and 2014, respectively. And she was a visiting scholar in Georgia Institute of Technology, Atlanta, GA, USA, during 2012-2013. She is currently a Professor with the State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China. She was the recipient of Distinguished Young Scholars of Hubei Province (2023), Young Elite Scientist Sponsorship Program by China Association for Science and Technology (2019), IEEE/ASME Transactions on Mechatronics Best Paper Finalist Award (2021), IEEE/ASME AIM Conference Best Paper Finalist Award (2020, 2016). She Her research interests include field reconstruction via flexible sensors, field-based sensing and monitoring.