CSE DSI Machine Learning Seminar with Yue Yu (Aerospace Engineering and Mechanics, UMN)

Autonomous Aerospace Systems in Shared Spaces

How can autonomous aerospace systems adapt to contingencies in real time? How can they learn to interact with each other? How can large populations of them coexist harmoniously with humans? Motivated by these questions, Yue Yu's research develops algorithmic solutions to help autonomous aerospace systems work in spaces shared with each other and with humans. In this talk, he will present three directions of his research focusing on single-agent, multiagent, and large populations of autonomous aerospace systems, respectively. First, he will present his research on real-time trajectory optimization via first-order optimization methods. NASA has selected these methods for their next-generation precision landing systems, with flight tests planned in 2025. Second, he will present learning algorithms that infer unknown objectives in noncooperative multiagent interactions, enabling autonomous systems to predict and strategically react to other agents’ decisions. Finally, he will discuss network infrastructure design problems for large-scale autonomy and their application in traffic and noise pollution management for urban air mobility. 

Yue Yu is an assistant professor in the Department of Aerospace Engineering and Mechanics. He received his Ph.D. in Aeronautics and Astronautics from the University of Washington in 2021. His research interests include optimization, control, game theory, learning, and transportation. He received the 2023 AIAA Guidance, Navigation, and Control Best Paper Award. He helped develop the optimization-based guidance software for the NASA Safe and Precise Landing-Integrated Capabilities Evolution system.