2019 Keynote Speakers/特邀报告

Prof. David Hsu, IEEE Fellow, National University of Singapore, Singapore

David Hsu is a professor of computer science at the National University of Singapore (NUS) and a member of NUS Graduate School for Integrative Sciences & Engineering. He received PhD in computer science from Stanford University. At NUS, he co-founded NUS Advanced Robotics Center and has been serving as the Deputy Director. He held visiting positions at MIT Aeronautics & Astronautics Department and CMU Robotics Institue. He is an IEEE Fellow. His research interests span robotics, AI, and computational structural biology. In recent years, he has been working on robot planning and learning under uncertainty and human-robot collaboration. He, together with colleagues and students, won the Humanitarian Robotics and Automation Technology Challenge Award at International Conference on Robotics & Automation (ICRA) 2015, the RoboCup Best Paper Award at International Conference on Intelligent Robots & Systems (IROS) 2015, and the Best Systems Paper Award at Robotics: Science & Systems (RSS), 2017. More information on his research is available on the M²AP research group web site. He has chaired or co-chaired several major international robotics conferences, including WAFR 2004 and 2010, RSS 2015, and ICRA 2016. He was an associate editor of IEEE Transactions on Robotics. He is currently serving on the editorial board of Journal of Artificial Intelligence Research.

 

Speech Title: Robots in Harmony with Humans

Abstract: Early robots often occupied tightly controlled environments, e.g., factory floors, designed to segregate robots and humans for safety. In the near future, robots will "live" with humans, providing a variety of services at homes, in workplaces, or on the road. To become effective and trustworthy collaborators, robots must adapt to human behaviors and more interestingly, adapt to changing human behaviors, as humans adapt as well. I will discuss our recent work, covering (i) mathematical models for human intentions, trust, ..., (ii) planning algorithms that connect robot perception with decision making, and (iii) learning algorithms that enable robots to adapt to human preferences. The discussion, I hope, will spur greater interest towards principled approaches that integrate perception, planning, and learning for fluid human-robot collaboration.

 

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