Prof. Makoto Iwasaki

IEEE Fellow, IEE Japan Fellow

Nagoya Institute of Technology, Japan

Biography: Makoto Iwasaki received his B.S., M.S., and Dr. Eng. degrees in Electrical and Computer Engineering from NIT in 1986, 1988, and 1991, respectively. He served as Vice President of Nagoya Institute of Technology from 2024 to 2025.
He is a distinguished leader within the IEEE Industrial Electronics Society (IES). His major service roles include Chair of the IES Fellow Evaluation Committee (2022–2023), Co‑Editor‑in‑Chief of IEEE Transactions on Industrial Electronics (2016–2022), and Vice President for Planning and Development (2018–2021). He was elevated to IEEE Fellow in 2015 for his contributions to fast and precise positioning in motion controller design. He is also a Fellow of the Institute of Electrical Engineers of Japan (IEEJ) and a member of the Science Council of Japan.
His scholarly contributions have been recognized with numerous prestigious awards, including Best Paper and Technical Awards from IEE Japan, the Nagamori Award, the Ichimura Prize, and the Commendation for Science and Technology from the Japanese Minister of Education. He was also listed among the World’s Top 2% Scientists (2024) by Stanford University and Elsevier.
His current research focuses on advanced control theory applied to linear and nonlinear modeling, precision positioning, and intelligent motion control, with strong and sustained collaboration with industry.  

Title of Speech: Motion Control for Industrial Positioning Devices with Strain Wave Gearing: Basics, Applications, and Beyond 

Abstract: This keynote speech presents practical motion control design methodologies for precision positioning systems incorporating strain wave gearing, such as industrial multi‑axis robots and high‑precision rotational positioning stages. Among various strain wave gear technologies, HarmonicDrive® gears (HDGs) are widely used; however, they inherently exhibit nonlinear characteristics known as Angular Transmission Errors (ATEs) arising from structural errors and elastic deformation within the mechanism. As a result, the theoretically achievable positioning accuracy implied by actuator encoder resolution cannot be fully realized at the gear output.
Furthermore, periodic disturbances induced by ATEs often excite mechanical resonances in HDG‑based systems, particularly when the frequencies of synchronous ATE components coincide with critical mechanical resonant modes. These phenomena lead to significant degradation in positioning accuracy and vibration performance, presenting fundamental challenges in high‑precision motion control.
To address these issues, this speech focuses on advanced motion controller design techniques aimed at mitigating ATE‑induced disturbances and suppressing resonant vibrations. Assuming that accurate mathematical models of ATEs can be identified, both model‑based feedforward compensation and robust feedback control strategies are introduced, along with key considerations regarding sensor placement and system architecture.
The proposed approaches have been implemented in practical precision motion systems, including servo actuators equipped with strain wave gearing. Their effectiveness has been validated through comprehensive numerical simulations and experimental studies conducted in close collaboration with industry partners. The keynote concludes by discussing future perspectives for next‑generation intelligent motion control systems utilizing strain wave gearing.  

Prof. Xingjian JING

City University of Hong Kong, Hong Kong

Biography: Xingjian Jing (M’13, SM’17) received the B.S. degree from Zhejiang University, China, the M.S. degree and PhD degree in Robotics from Shenyang Institute of Automation, Chinese Academy of Sciences, respectively. He also achieved the PhD degree in nonlinear systems and signal processing from University of Sheffield, U.K..
He is now a Professor with the Department of Mechanical Engineering, City University of Hong Kong. Before joining in CityU, he was a Research Fellow with the Institute of Sound and Vibration Research, University of Southampton, followed by assistant professor and associate professor with Hong Kong Polytechnic University. His current research interests include: Nonlinear dynamics, Vibration, Control and Robotics, with a series of 280+ publications of 16000+ citations and H-index 67 (in Google Scholar), with a number of patents filed in China and US. He is one of the world top2% highly cited scientists and IEEE senior member.
Prof Jing is the recipient of a number of academic and professional awards including 2016 IEEE SMC Andrew P. Sage Best Transactions Paper Award, 2017 TechConnect World Innovation Award in US, 2017 EASD Senior Research Prize in Europe and 2017 the First Prize of HK Construction Industry Council Innovation Award, etc.
He currently serves (or served) Senior Editor of Mechanical Systems and Signal Processing, Topic Associate Editor of Nonlinear Dynamics, and Associate Editors of IEEE Transactions on Systems, Man, Cybernetics -Systems, IEEE Transactions on Industrial Electronics (2021-2024), and Technical Editor of IEEE/ASME Trans. on Mechatronics (2015-2020). He was lead editors of special issues on “Exploring nonlinear benefits in engineering” during 2018-2019 and “Next-generation vibration control exploiting nonlinearities” during 2021-2022 both published in Mechanical Systems and Signal Processing. He is the general conference chair of ICANDVC since 2021.  

Title of Speech: Human-Agent Shared-Control Based Learning Strategies for More Efficient Robotic Tadpole Navigation 

Abstract: Biomimetic robotic tadpoles provide a promising platform for aquatic monitoring, inspection, and exploration, yet reliable autonomy remains difficult because oscillatory propulsion and fluid-structure interactions induce nonlinear, strongly coupled, and disturbance-sensitive dynamics. In practice, reinforcement learning for such systems is constrained by three core bottlenecks: sample inefficiency, inadequate generalization beyond the training distribution, and persistent sim-to-real gaps under real aquatic uncertainties. To address these challenges, we present a family of risk-aware human-agent shared-control variants for efficient robotic tadpole navigation, spanning an online-only human-guided framework and a dual-mode framework that integrates offline expert priors with online corrective supervision.
The central idea is to treat human guidance not as a temporary rescue signal, but as a structured source of knowledge that can be internalized into policy learning and deployment adaptation. In the online variant, human intervention is triggered selectively in high-risk or out-of-distribution states through uncertainty-aware authority arbitration, and is incorporated through prioritized replay, intervention-aware reward shaping, and clone-based policy updates. In the dual-mode variant, offline demonstrations are further distilled into expert priors and injected via KL-regularized alignment, enabling safer and more data-efficient early policy formation while preserving the benefits of online corrective supervision. Together, these mechanisms establish a scalable foundation for a unified learning-to-deployment system that explicitly supports deployment-stage recovery and stable execution in long-horizon, low-tolerance real-world aquatic issues under uncertainty.
Experimental results demonstrate superior training efficiency, generalization, and reduced human reliance over advanced RL strategies, enabling dynamics-invariant, trajectory-invariant, and demonstration scale-invariant, and failure-recoverable deployment performance. Taken together, the proposed risk-aware human-guided shared control variants point toward a broader future in which embodied AI systems do not merely execute learned policies, but recover, adapt, and sustain reliable autonomy in the face of real-world uncertainty.  

Assoc. Prof. Weiwei Wan

The University of Osaka, Japan

Biography: Weiwei Wan is an associate professor working at the School of Engineering Science, Osaka University, Japan. He is an IEEE senior member, having affiliations with the IEEE Robotics and Automation Society, and the IEEE System, Man, and Cybernetics Society. He is also a member of RSJ (the Robotics Society of Japan) and JSME (Japan Society of Mechanical Engineers). Weiwei Wan's major interest is smart manufacturing using single or multiple robotic manipulators: Developing and deploying grasping planning, motion planning, and other low level and high level task planning algorithms for next-generation factories. He is also studying visual perception, force control, and learning approaches to make up for the inherent shortages of planning algorithms.  

Title of Speech: AI4S: Robot Arm working with Plant Phenotyping 

Abstract: This talk introduces a robotic arm-based plant phenotyping system developed through collaboration between our group and RIKEN. The system aims to replace repetitive manual operations in plant science experiments, including liquid dispensing, tissue sampling, and cryogenic preservation. To achieve these tasks, we developed several key modules: a vision system for detecting plant leaves, stems, and petioles; robotic grippers for liquid handling and scissor manipulation; and manipulation functions for handling tubes, picking sampled leaves, and supporting downstream preservation procedures. In this talk, I will present these components in detail and discuss how AI and robotics can contribute to scientific discovery.