CIS Seminar Series 2026 - Haimin Hu, PhD

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Game-theoretic safety and agility for human-centered robotics

ABSTRACT From autonomous vehicles navigating busy intersections to mobile manipulators deployed in household environments, robots must operate safely and efficiently around people in uncertain and unstructured situations. Yet today’s robots still struggle to robustly handle human-induced uncertainty without sacrificing efficiency when working closely with people. This talk will argue that jointly reasoning the robots' physical contexts and belief states that capture human uncertainty offers a unifying lens for trustworthy human–robot interaction (HRI). We begin by introducing a belief-space safety filtering framework that combines robust safety analysis with probabilistic inference to enable assured interactions across diverse HRI scenarios. We will then show how such filters can be scalably synthesized via game-theoretic reinforcement learning and statistically verified using conformal prediction. Finally, we present a belief-space dynamic game in which a robot coach helps a human learner acquire and retain motor skills.

BIOGRAPHY Haimin Hu is an assistant professor of computer science and a core member of the Johns Hopkins Data Science and AI Institute, the Laboratory for Computational Sensing and Robotics, and the Institute for Assured Autonomy. His research focuses on the algorithmic foundations of human-centered autonomy. By integrating dynamic game theory with machine learning and safety-critical control, his work enables deployable, verifiable, and trustworthy systems, from autonomous vehicles to drones and legged robots. He has received several awards and recognitions, including being named a 2026 Penn AI Fellow, a 2025 Robotics: Science and Systems Pioneer, a 2025 Cyber-Physical Systems Rising Star, and a 2024 Human-Robot Interaction Pioneer. He has worked closely with industry partners such as the Toyota Research Institute and the Honda Research Institute to develop next-generation human-centered autonomy. Currently, he serves as an Associate Editor for IEEE Robotics and Automation Letters. He received a PhD (2025) and an MA (2022) in Electrical and Computer Engineering from Princeton University, an MSE (2020) in Electrical Engineering from the University of Pennsylvania, and a BEng (2018) in Electronic and Information Engineering from ShanghaiTech University. From 2025 to 2026, he was a postdoctoral fellow with the PRECISE Center and the GRASP Lab at the University of Pennsylvania. Computer & Information