Many of the most promising applications for autonomous robots require them to interact with other agents (robots or humans) whose objectives, goals, or incentives may be different from their own. Collision avoidance for drone delivery fleets, autonomous driving in traffic, and human-robot teaming are all example of multi-agent interactions in which the multiple agents may not share the same objectives. Game theory provides a natural framework through which to understand, analyze, and design algorithms for multiple interacting robotic agents in such scenarios. In this talk I will describe several recent examples from my lab of game-theoretic algorithms enabling robots to interact with other agents in collaborative and competitive scenarios. I will describe an algorithm for a group of pursuing drones to capture a group of fleeing drones in finite time. I will describe a highly scalable distributed collision avoidance algorithm suitable for drones and autonomous cars. Finally, I will present a game theoretic receding horizon control algorithm for autonomous drone racing and car racing. I will show results from hardware experiments with ground robots, scale autonomous cars, and quadrotor UAVs collaborating and competing in the scenarios above.

About the Speaker

Mac Schwager is an assistant professor of Aeronautics and Astronautics at Stanford University. He directs the Multi-robot Systems Lab (MSL) where he studies distributed algorithms for control, perception, and learning in groups of robots and autonomous systems. He is interested in a range of applications including cooperative surveillance with teams of UAVs, agile formation control and collision avoidance for UAVs, autonomous driving in traffic, cooperative robotic manipulation, and autonomous drone racing. He obtained his BS degree from Stanford, and his MS and PhD degrees from MIT. He was a postdoctoral researcher in the GRASP lab at the University of Pennsylvania, and in CSAIL at MIT. Prior to joining Stanford, he was an assistant professor at Boston University from 2012 to 2015. He received the NSF CAREER award in 2014, the DARPA YFA in 2018, and has received numerous best paper awards in conferences and journals including the IEEE Transactions on Robotics King-Sun Fu best paper award in 2016.