Autonomous systems are becoming more and more prevalent; meanwhile, both industry and academia are pushing the fundamental limits of what these systems can do. As autonomous systems become more sophisticated and tightly coupled to human society, their safety and effectiveness rely increasingly upon accurate modeling and prediction of human behavior. This talk will touch on work at multiple levels of abstraction, including low-level robust control and higher-level confidence-aware human prediction and motion planning. We will focus, however, on a new solution strategy for solving nonlinear N-player general-sum differential games, which arise in many motion planning problems of practical interest. The proposed algorithm is easily real-time, even for moderately large problems. We will discuss the core intuition behind the proposed approach, present convergence and complexity results, and demonstrate it through several examples.
David Fridovich-Keil is a fifth year PhD student at UC Berkeley, working on autonomous robotics, robust control, and differential game theory. He is advised by Prof. Claire Tomlin in the Hybrid Systems Lab. David is also a member of the Berkeley AI Research lab, and is supported by an NSF Graduate Research Fellowship. David did his undergrad in electrical engineering at Princeton.
CS Faculty Host: Kris Hauser