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Keynote Speakers-EECR 2024

Wei-Hsin Liao
Choh-Ming Li Professor of Mechanical and Automation Engineering,
Department Chairman and Graduate Division Head;
Director, Institute of Intelligent Design and Manufacturing, the Chinese University of Hong Kong

Biography: Wei-Hsin Liao received his Ph.D. in Mechanical Engineering from The Pennsylvania State University, University Park, USA. At Penn State University, he received the Inventor Incentive Award and Sigma Xi Graduate Research Award. Since August 1997, Dr. Liao has been with The Chinese University of Hong Kong (CUHK), where he is currently Department Chairman and Choh-Ming Li Professor of Mechanical and Automation Engineering. His research has led to publications of over 360 technical papers in international journals and conference proceedings, and 23 patents. He was the Conference Chair for the 20th International Conference on Adaptive Structures and Technologies in 2009; the Active and Passive Smart Structures and Integrated Systems, SPIE Smart Structures/NDE in 2014 and 2015. He received the T A Stewart-Dyer/F H Trevithick Prize 2005, awarded by the Institution of Mechanical Engineers (IMechE). He is a recipient of the 2008 Best Paper Award in Structures, 2017 Best Paper Award in Mechanics and Material Systems, and 2021 Energy Harvesting Best Paper Award,  from the American Society of Mechanical Engineers (ASME). He also received four best paper awards in IEEE international conferences. At CUHK, Prof. Liao was awarded the Young Researcher Award 2007, the Research Excellence Award (2010-11), and Outstanding Fellow of the Faculty of Engineering (2014-19). As the Chair of Joint Chapter of Robotics, Automation and Control Systems Society (RACS), IEEE Hong Kong Section, Dr. Liao received 2012 Chapter of the Year Award from the IEEE Robotics and Automation Society. Prof. Liao is the recipient of 2020 ASME Adaptive Structures and Material Systems Award and 2018 SPIE SSM Lifetime Achievement Award, to recognize his outstanding contributions to the advancement of smart structures and materials. He is also the recipient of 2023 ASME Leonardo Da Vinci Award for the eminent achievement in the design and invention which are universally recognized as an important advance in machine design. He currently serves as an Associate Editor for Journal of Intelligent Material Systems and Structures, and on the Executive Editorial Board of Smart Materials and Structures. Dr. Liao is a Fellow of ASME, HKIE, and IOP.

Speech Title: Robotic Exoskeletons for Motion Assistance and Load Transportation

Abstract: Exoskeletons are considered as promising devices for motion assistance of mobility impaired patients and motor ability augmentation of healthy people. Considering the interactive action between exoskeletons and human body, a safe and comfortable human-exoskeleton interaction is essential to achieve effective exoskeleton operations for human motion assistance. We designed a proxy-based torque controller (PTC) for safe and performant interactive torque control of motor-driven exoskeletons under different scenarios. A generalized model was built and analyzed for commonly applied motor-based conventional stiff actuator (CSA) and series elastic actuator (SEA) for exoskeletons. Then, the PTC with specified compensation was designed based on proxy-based sliding mode control strategy and the stability was theoretically proved for the closed-loop system of the two types of actuators. Meanwhile, an adaptive law was designed for online adjustment of the PTC parameter to produce an overdamped system response for the two actuators under unexpected interruptions. Following the adaptive law, the PTC robustly realized a compliant torque control while keeping accurate torque tracking under normal operation. Experiments were conducted for two back-support exoskeletons with CSA and SEA, verifying the effectiveness of the proposed controller for safe human-exoskeleton interaction guarantee under different scenarios. In this talk, the developed devices/systems and key results will be presented.

 

Shane Xie
Chair in Robotics+Autonomous Systems
University of Leeds, United Kingdom

Biography: Prof Shane (Sheng Q) Xie, Ph.D., FIPENZ, is the Chair of Robotics and Autonomous Systems and Director of the Rehabilitation Robotics Lab at the University of Leeds, and he was the Director of the Rehabilitation and Medical Robotics Centre at the University of Auckland, New Zealand (NZ, 2002-2016). He has >28 years of research experience in healthcare robotics and exoskeletons. He has published > 400 refereed papers and 8 books in rehabilitation exoskeleton design and control, neuromuscular modelling, and advanced human-robot interaction. He has supervised >15 postdocs, 62 PhDs and 80 MEs in his team with funding of >£27M from five countries since 2003. His team has invented three award-winning rehabilitation exoskeletons. He is an expert in control of exoskeletons, i.e. impedance control, adaptive control, sliding mode control, and iterative learning control strategies. He has received many distinguished awards including the New Zealand Science Challenge Award, the David Bensted Fellowship Award, and the AMP Invention Award. He is an elected Fellow of the Institute of Professional Engineers of New Zealand and the Technical Editor for IEEE/ASME Transaction on Mechatronics.

Speech Title: Advanced Robotics with Enhanced Autonomy and Intelligence for Effective Medical Rehabilitation

Abstract: Globally, 15M people suffer a stroke every year, causing 6M deaths and leaving another 5M permanently disabled, which makes stroke the second leading cause of disability worldwide . In the UK, strokes affect over 152,000 Britons each year. Currently, there are over 1.2 million people living with the effects of stroke in the UK, and the estimated direct and indirect costs of stroke care for the NHS are >£9 billion a year. According to UK Guidelines for stroke rehabilitation, patients should receive at least 45 minutes of therapy per day for a minimum of 5 days per week; however, this standard has never been met due to the deceasing availability of rehabilitation services and increasing pressure on the NHS. In the UK, there are > 600,000 stroke patients that live further than 20km from a stroke support group, and the majority of them have severe mobility issues, it would be very challenging and costly, or even impossible for them to travel and receive regular rehabilitation treatment in hospitals or rehabilitation centers. The NHS' Five Year Forward View made recommendations in 2017 to bring rehabilitation to people in their own homes and care homes.
This talk will discuss the key societal challenges and robotic technologies for delivering effective care and opportunities for the healthcare industry. It will cover the recent development of robotics for stroke rehabilitation, the research gaps and the need for new technologies in neuroscience, robotics, control and artificial intelligence. The talk will also introduce a EPSRC-funded project on intelligent reconfigurable exoskeletons tailored to meet patients’ needs, deliver effective diagnosis and personalised treatment, and monitored remotely by rehabilitation therapists. The key projects conducted at the Leeds Centre for Assistive/Rehabilitation Robotics will be introduced including  peanumatic Peano muscle, DEA, soft exoskeleton, bilaterial robot, neuromuscular and brain computer interfaces. The focus is placed on the enabling technologies for those whose strength and coordination have been affected by amputation, stroke, spinal cord injury, cerebral palsy and ageing.

 

Yu Liu
South China University of Technology, China

Biography: Dr. Yu Liu received the Ph.D. degree from the South China University of Technology in 2009. He was a visiting student with the Department of Mechanical Engineering, Concordia University, Montreal, QC, Canada, from 2008 to 2009, and a Visiting Scholar with the Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE, USA, from 2017 to 2018.
Dr. Liu is currently a professor with the South China University of Technology, Distinguished Professor of Chang Jiang Scholars, Ministry of Education of China, and Outstanding Talent of Guangdong Special Support Plan. He has won the First Class Prize of Technology Invention Awards of Guangdong Provincial (Ranked First), the Second Class Prize of Natural Science Awards of Guangdong Province (Ranked First) and the First Class Prize of Science and Technology Progress Awards of Chinese Association of Automation (Ranked First). He has obtained many research grants, including National Key R&D Program of China, National Natural funds, Key R&D Program of Guangdong Province, etc. He has authored two books and over twenty patents, and published over 100 international journal and conference papers (more than 80 SCI papers). His current research interests include robotics, intelligent control system and machine vision.
He is serving as an Associate Editor for the IEEE/ASME Transactions on Mechatronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems and IEEE Transactions on Computational Social Systems.

Speech Title: Intelligent Control, Visual Detection and Fusion Localization in Robotics

Abstract: With the development of artificial intelligence, robots are playing an increasingly important role in many fields. Although robot control and perception technology can achieve partial intelligence, it still faces many problems when enabling robots to autonomously perform complex tasks. In complex scenes, the grasping and vibration suppression of robots require the support of intelligent control. Robots equipped with high-precision sensors are the foundation of visual detection and localization. Considering the complex scenes and high-speed dynamic effects during robots in motion, we propose visual detection technologies and robust localization methods for robots in industrial manufacturing scenes to improve the reliability of performing autonomous tasks. This report introduces the intelligent control, visual detection, and fusion localization problems when robots perform complex tasks, and provides corresponding research results.