Quanyan Zhu MAD Games Workshop at ICRA 2024

MAD Games workshop on Multi-Agent Dynamic Games at ICRA 2024 was organized by Rahul Mangharam, Hongrui Zheng, Shuo Yang, Johannes Betz and Venkat Krovi. icra2024-madga...
Multiple robots are often required to collectively complete complex tasks in smart manufacturing, transportation systems, and human-robot collaborations. Teaming and coordination are essential to enable collective intelligence, facilitate the execution of complex tasks, improve task efficiency, and enhance system robustness. As agents exhibit heterogeneous behavior, there is a crucial need for a strategic viewpoint and approaches to effectively coordinate them and develop fast and effective coordination and learning algorithms. In this talk, we discuss a human-vehicle teaming and planning problem in the context of assistive driving systems, where humans and vehicles collaborate and share controls. The problem is challenging due to environmental uncertainties, human bounded rationality, and variability in human behaviors. An effective collaboration plan based on a Stackelberg meta-learning algorithm is developed to learn and adapt to uncertainties and assist the human driver in reaching the target destination.
Bio: Quanyan Zhu received B. Eng. in Honors Electrical Engineering from McGill University in 2006, M. A. Sc. from the University of Toronto in 2008, and Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) in 2013. After stints at Princeton University, he is currently an associate professor at the Department of Electrical and Computer Engineering, New York University (NYU). He is an affiliated faculty member of the Center for Urban Science and Progress (CUSP) and Center for Cyber Security (CCS) at NYU. His research interests include cyber and physical systems, multi-agent systems, and cybersecurity and resilience. He currently serves as the technical committee chair on security and privacy for the IEEE Control Systems Society.