Please note the location of this event has changed to B-17 Hackerman Hall.
Abstract
From autonomous vehicles navigating busy intersections to quadrupeds deployed in household environments, robots must operate safely and efficiently around people in uncertain and unstructured situations. However, today’s robots still struggle to robustly handle low-probability events without becoming overly conservative. In this talk, Haimin Hu will discuss how planning in the joint space of physical and information states (e.g., beliefs) enables robots to make safe, adaptive decisions in human-centered scenarios. He will begin by introducing a unified safety filter framework that combines robust safety analysis with probabilistic reasoning to enable trustworthy human–robot interaction. He will discuss how robots can reduce conservativeness without compromising safety by closing the interaction–learning loop. Next, Hu will show how game-theoretic reinforcement learning tractably synthesizes a safety filter for high-dimensional systems, guarantees training convergence, and reduces the policy’s exploitability. Finally, he will present an algorithmic approach to scaling up game-theoretic planning for resolving conflicts and optimizing social welfare for strategic interactions involving many agents. Hu will conclude with a vision for next-generation human-centered robotic systems that actively align with their human peers and enjoy verifiable safety assurances.
Speaker Biography
Haimin Hu is a final-year PhD candidate in electrical and computer engineering at Princeton University. His research integrates dynamic game theory with control systems safety and reinforcement learning to enable trustworthy human–robot interaction. Prior to his doctoral studies, Hu received his MSE. degree in electrical engineering from the University of Pennsylvania in 2020 and a BE degree in electronic and information engineering from ShanghaiTech University in 2018. From 2017 to 2018, he was a visiting student in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Hu has worked at the Toyota Research Institute, the Honda Research Institute, and the National Institute for Nuclear Physics in Padova, Italy, and he currently serves as an associate editor for IEEE Robotics and Automation Letters. In 2024, Hu was named a Human–Robot Interaction Pioneer by the Institute of Electrical and Electronics Engineers and the ACM.