InformatiKOM I, Bldg. 50.19, Atrium, Adenauerring 12

27th of March 2026, 15:00

David Hsu is a Provost’s Chair Professor in the Department of Computer Science, National University of Singapore, the director of Smart Systems Institute, and also the founding director of NUS Artificial Intelligence Laboratory. He received BSc in computer science & mathematics from the University of British Columbia, Canada, and PhD in computer science from Stanford University, USA. He is an IEEE Fellow.

His research lies in the intersection of robotics and AI. In recent years, he has been working on robot planning and learning under uncertainty for human-centered robots. His work won multiple international awards, including, most recently, Test of Time Award at Robotics: Science & Systems (RSS) in 2021 and IJCAI-JAIR Best Paper Prize in 2022.

Abstract

A hallmark of intelligence is the ability to do the right thing in myriad unfamiliar situations. The classic model-based approach to robotics draws a sharp boundary between the closed, known world and the open, unknown world: robot performance is guaranteed only in known situations.  Data-driven robot foundation models, with their vast common-sense knowledge, have blurred this boundary and dramatically expanded robot capabilities in the open world. In this talk, I will argue that the goal of scalable and robust robot intelligence necessitates an integration of model-based planning and data-driven learning. The key question here is the interplay, rather than the conflict, between structure and data. I will illustrate the general thinking with our work on robots aiming to navigate "anywhere", robots folding a variety of clothes, and robots operating in the open world by generating and verifying hypotheses.