Program & Events
Plenary Talks
Plenary Talk 1
Awareness captured through biological signals analysis
Professor Eiryo Kawakami
RIKEN/Chiba University
Humans process a vast amount of information from the inside and outside of their bodies and take actions according to the situation. Even in everyday actions such as walking, a large amount of information from the senses of sight, hearing, touch (from the soles of the feet), and muscle sensation is unconsciously integrated to produce smooth coordinated movement of the arms and limbs. On the other hand, when people take up a new sport or rehabilitation, they need to be aware of how to move their bodies in a way they have never done before and consciously change their behavior. In addition, when they are unable to perform actions that they were previously able to do unconsciously due to illness or aging, or when their bodies do not move as they expect, a sense of discomfort becomes apparent. To realize robot-mediated human support, it is necessary to understand the characteristics of human biological signals and to quantitatively capture “awareness” given to humans via robots and “discomfort” that should be supported by robots. In this presentation, I would like to discuss research strategies to quantitatively capture the state of humans based on measurement of biological signals.
After graduating from the Faculty of Medicine at the University of Tokyo in 2007, he went straight into graduate school without doing initial training for physician. In 2011, he received a doctorate in medicine from the Graduate School of Medicine at the University of Tokyo (Yoshihiro Kawaoka Laboratory). Since 2013, he has been engaged in research in systems biology at the RIKEN Hiroaki Kitano Laboratory, and since 2016 he has been participating in the RIKEN Medical Science Innovation Hub Promotion Program, which is supported by JST. Since 2017, he has been the unit leader of the same program. In 2019, he became a professor of Artificial Intelligence (AI) medicine at the Graduate School of Medicine, Chiba University, and Director of the Center for Artificial Intelligence (AI) Research in Therapeutics. Also, from the same year, he has been concurrently serving as a team leader at RIKEN through cross-appointment. He is a leading researcher in data-driven medical research, developing research that makes full use of systems biology and machine learning to address issues in clinical and basic medicine. He was selected as a “Nice Step Researcher 2019” by the National Institute of Science and Technology Policy.
Plenary Talk 2
Learning-based Control with Application to Robotic Systems
Professor Lihua Xie
Nanyang Technological University
Reinforcement learning where the agent learns a policy to optimize a pre-defined reward by interacting with the environment has undergone rapid development and found applications in many areas such as robot navigation and manipulation. In this talk, we focus on inverse reinforcement learning (IRL) where agent learns an appropriate cost function, leading to desired behaviours, based on demonstration data. We introduce a differential dynamic programming (DDP)-based framework for IRL with both open-loop and closed-loop costs and demonstrate that the closed-loop method is better than the open-loop one. We further discuss a learning-based dynamic weight adjustment scheme for robots operating in human-dense environments. The applications of the learning-based framework in UAVs and ground robots will be demonstrated.
Lihua Xie obtained his PhD degree from the University of Newcastle, Australia, in 1992. He is currently President’s Chair in Control Engineering with the School of Electrical and Electronic Engineering, Nanyang Technological University and Director, National Research Foundation Center for Advanced Robotics Technology Innovation (CARTIN). He has served as Head of Control and Instrumentation Division and Director of Delta-NTU Corporate Laboratory for Cyber-Physical Systems. His research areas include robust control, multi-agent systems, learning-based control, and unmanned systems. He is currently an Editor-in-Chief of Unmanned Systems and has served as an Editor of IET Book Series on Control and Associate Editor of IEEE Transactions on Automatic Control, Automatica, IEEE Transactions on Control Systems Technology, IEEE Transactions on Control of Network Systems, etc. He was an IEEE Distinguished Lecturer (2011-2014) and the General Chair of the 62nd IEEE Conference on Decision and Control. He is currently Vice-President of IEEE Control System Society. Professor Xie is Fellow of Academy of Engineering Singapore and Fellow of IEEE, IFAC, CAA and AAIA.
Plenary Talk 3
TBA