Speakers

As of May 9, 2025

Plenary Speakers

Professor Hong Qiao

Institute of Automation, Chinese Academy of Sciences, Beijing, China.

Speaker Introduction

Prof. Hong Qiao (Fellow, IEEE) received the B.Eng. degree in hydraulics and control and the M.Eng. degree in robotics from Xi’an Jiaotong University, Xi’an, China, in 1986 and 1989, respectively, the M.Phil. degree in robotics control from the Industrial Control Center, University of Strathclyde, Strathclyde, U.K., in 1992, and the Ph.D. degree in robotics and artificial intelligence from De Montfort University, Leicester, U.K., in 1995. She was selected as Academician of Chinese Academy of Sciences in 2021. She is currently a Professor with the State Key Laboratory of Multimodal Artificial Intelligence System, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Her current research interests include brain-inspired intelligent robots, information-based strategy investigation, machine learning, and pattern recognition.

Professor Yun-Hui Liu

Choh-Ming Li Professor of Mechanical and Automation Engineering Director, T Stone Robotics Institute The Chinese University of Hong Kong & Director, Hong Kong Centre for Logistics Robotics

Abstract Title: AI-empowered Surgical Robots

Robots have been widely used to help surgeons in various surgical procedures. Existing surgical robots are controlled by surgeons via control interfaces. Human control may cause concerns like surgeon-dependent performance/quality, safety risk due to fatigue, etc. The rapid development of AI, in particular embodied AI, presents a lot of opportunity for introducing AI-powered perception and automation to robotic surgery, which would reduce the workload of surgeons, maintain consistence in surgical outcomes, and improve the quality of operations. In this talk, we will introduce our on-going project: AI-powered Surgical Robots, funded by the RGC Area of Excellence Scheme, which aims to develop AI-powered technologies for surgical scene recognition, surgical skill learning, planning and control, and integrated surgical robots with high-level autonomy.


Speaker Introduction

Yun-hui Liu received B. Eng. degree in Applied Dynamics from Beijing Institute of Technology, M. Eng. degree in Mechanical Engineering from Osaka University, and Ph.D. degree in Applied Mathematics from the University of Tokyo. After working at the national Electrotechnical Laboratory of Japan as a Research Scientist, he joined The Chinese University of Hong Kong (CUHK) and is currently a Choh-Ming Li Professor of Mechanical and Automation Engineering, the Director of the CUHK T Stone Robotics Institute, and the Director/CEO of Hong Kong Centre for Logistics Robotics funded by the InnoHK clusters of the HKSAR government. He has published more than 500 papers in refereed journals and conference proceedings and was listed in the Highly Cited Authors (Engineering) by Thomson Reuters. His research interests include vision-based robotics, machine intelligence and their applications in manufacturing, logistics, healthcare and constructions. Prof. Liu has received numerous research awards from international journals and international conferences in robotics and automation, and from government agencies. In recent years, he has been actively transferring robotics technologies developed at university labs to industries, and co-founded VisionNav Robotics, CornerStone Robotics, etc. He was the Editor-in-Chief of Robotics and Biomimetics and served as an Associate Editor of the IEEE Transactions on Robotics and Automation. He is Fellow of IEEE, HKIE and HKAE.

Professor Kensuke Harada

Department of System Innovation, Osaka University

Abstractin this talk, we present our recent progress on robotic manipulation research aiming for industrial applications done in our lab. The robotic manipulation is very important in industry to realize high-mix/low-volume production, logistics automation, laboratory automation, food industry etc. We introduce several aspects of research done in our lab. including task/motion planning, assembly, picking, machine learning and gripper design.


Speaker Introduction

Prof. Kensuke Harada (Fellow, IEEE) received the Ph.D. degree from the Graduate School of Mechanical Engineering, Kyoto University, Kyoto, Japan, in 1997. He is currently a Professor working at the Graduate School of Engineering Science, Osaka University. From 1997 to 2002, he was a Research Associate at Graduate Industrial and Systems Engineering, Hiroshima University, Hiroshima, Japan. From 2005 to 2006, he was a Visiting Scholar at the Computer Science Department, Stanford University, CA, USA. Before joining Osaka University, he was a Researcher at National Inst. of AIST, Tsukuba, Japan. His research interests include mechanics and control of humanoid robots and robotic hands.

Professor Shinichi Hirai

Department of Robotics, Ritsumeikan University

Speaker Introduction

Shinichi Hirai received his Ph.D degree in applied mathematics and physics from Kyoto University in 1991. He joined the newly established Department of Robotics at Ritsumeikan University in 1996. Since 2002, he has been a Professor in the department. He was a Visiting Researcher with the Massachusetts Institute of Technology in 1989 and was an Assistant Professor with Osaka University from 1990 to 1996. His current research interests include soft robotic hands, soft sensors, soft object manipulation, and soft object modeling. He received the Robotics Society of Japan (RSJ) Best Paper Award in 2008, FOOMA Japan Academic Plaza Award in 2018, and Int. Conf. on Ubiquitous Robots Best Paper Award in 2020. He is a member of IEEE, RSJ, JSME, and SICE.

Keynote Speakers

Prof. Dr. Wenying Xu

Research Center for Complex Systems and Network Sciences, School of Mathematics, Southeast University, China.

Abstract Title： Communication-Efficient Distributed Control and Optimization in Multi-Agent Systems
As multi-agent systems (MAS) become increasingly complex and large-scale—spanning applications such as autonomous vehicles, sensor networks, and swarm robotics—there is a pressing need for coordination strategies that are not only robust and scalable, but also highly efficient in communication. This talk presents recent advances in distributed control and optimization algorithms designed to minimize communication overhead while preserving strong performance guarantees. We begin by introducing several event-triggered communication mechanisms that ensure system stability and consensus with minimal data exchange. Building on this, we explore adaptive control and optimization techniques that enhance scalability in dynamic and uncertain environments. Particular focus is placed on addressing challenges posed by complex network topologies, open and time-varying communication structures, and resilience to cyber-attacks. Theoretical developments are illustrated through practical applications, demonstrating the trade-offs among communication efficiency, convergence speed, and control accuracy. The presented work aims to advance the foundation for scalable, secure, and intelligent decision-making in modern networked systems operating under real-world constraints.


Speaker Introduction

Wenying Xu is a Professor in the Department of System Science, Southeast University. She received her Ph.D. from City University of Hong Kong in 2017, and her M.S. degree from Southeast University, China, in 2014. Prior to her current position, She was a Research Fellow in the School of Electrical and Electronic Engineering, Nanyang Technological University from 2017 to 2018 and was an Academic Visitor in the Department of Computer Science, Brunel University London, from May 2015 to Aug. 2015 and from Oct. 2019 to Dec. 2019. She was a Post-Doctoral Fellow in Potsdam Institute for Climate Impact Research, Potsdam, Germany from 2019 to 2021, and an Associate Professor in the Department of System Science, Southeast University from 2020 to 2023. Her research interests include cyber-physical system, game theory in networks, distributed event-triggered control, and distributed cooperative control. Dr. Xu was a recipient of an Alexander von Humboldt Fellowship in 2018.

Assistant Professor Haoang Li

Thrust of Robotics and Autonomous Systems
Thrust of Intelligent Transportation，
The Hong Kong University of Science and Technology (Guangzhou), China.

Abstract Title: Embodied Robot Navigation and Manipulation in Complex Environments
Vision-language navigation and manipulation are two important research topics in the field of embodied robotics. Current research often assumes ideal environments, overlooking various complex factors present in real-world scenarios. This report will share our latest work aimed at complex environments. In terms of vision-language navigation, to address challenges such as cross-floor navigation, long sequences, and limited viewpoints, we proposed a method with spatial awareness and viewpoint robustness. By constructing multi-level maps, utilizing heterogenous graph attention mechanisms, and synthesizing new viewpoints, the navigation success rate of robots in complex environments is improved. In terms of vision-language manipulation, to tackle challenges such as potential network attacks, low instruction relevance, and long task sequences, we proposed a method with high fault tolerance and efficiency. By introducing implicit spatial encoding, action chunking, and parallel encoding techniques, the operational robustness of robots in complex environments is enhanced.


Speaker Introduction

Haoang Li received the B.E. and M.E. degrees from the School of Remote Sensing and Information Engineering, Wuhan University, China, in 2016 and 2018, respectively, and the Ph.D. degree from the Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, in 2022. He is currently an Assistant Professor of the Thrust of Robotics and Autonomous Systems and Thrust of Intelligent Transportation, The Hong Kong University of Science and Technology (Guangzhou), China. Before that, he was a Postdoc researcher with the School of Computation, Information and Technology, Technical University of Munich, Germany from 2022 to 2024. He was a visiting PhD student with the Department of Computer Science, ETH Zurich, Switzerland in 2021. His research interests include computer vision and robotics. He is currently serving as the Associate Editor of IEEE ICRA, and an organizing committee member for IEEE/RSJ IROS 2025. He received IEEE ICCV Doctoral Consortium Award and third place in IEEE ICRA RoboDrive Challenge.

Professor . Yue Wang 

Department of Control Science and Engineering
Zhejiang University

Abstract Title: Rethink robot navigation in the AI era: Is global consistency necessary?
Over the past decade, the deployment of mobile robots in industry has seen remarkable progress, largely enabled by advancements in globally consistent Simultaneous Localization and Mapping (SLAM) systems, which serve as the backbone for robot localization and path planning. Nevertheless, critical challenges persist when envisioning robots operating autonomously over extended lifespans in human-centric environments like households. Persistent environmental dynamics and continuous streams of sensory data render the maintenance of globally consistent maps increasingly impractical. In this talk, we first present our efforts to address these challenges by improving the loop closure and state estimators that prioritize long-term robustness. Second, inspired by emerging paradigms in embodied AI for navigation, we critically rethink the necessity of global consistency in lifelong operation. By analyzing the complementary strengths of classical SLAM-based navigation and data-driven AI approaches, we try to bridge the gap and introduce a hybrid framework, and explore its performance in navigation benchmarks and dynamic environments.


Speaker Introduction

Yue Wang works as a professor in the Department of Control Science and Engineering at Zhejiang University. His research has yielded over 50 publications in top-tier journals and conferences, including Nature Communications, the International Journal of Robotics Research, and IEEE Transactions on Robotics. He is a recipient of the Best Paper Award in Robot Vision at ICRA 2024 and a Best Paper Finalist in Robot Mechanism and Design at IROS 2023. He actively contributes to the robotics community as an Associate Editor for IEEE Robotics and Automation Letters and flagship conferences ICRA and IROS. His current research interest is robot learning for lifelong navigation and generalist manipulation.

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