MAD Games – Multi-Agent Dynamic Games: What can you learn from Autonomous Racing?

Abstract: Balancing performance and safety are crucial to deploying autonomous vehicles in multi-agent environments. In particular, autonomous racing is a domain that penalizes safe but conservative policies, highlighting the need for robust, adaptive strategies. Current approaches either make simplifying assumptions about other agents or lack robust mechanisms for online adaptation. In this talk we will explore research themes on perception, planning and control at the limits of performance. We explore:

(1) How to generate the most competitive agents who dynamically balance safety and assertiveness by using distributionally robust online adaptation and Game-theoretic planning
(2) How to be better-than-the-best using imitation learning with multiple imperfect experts
(3) Using invertible neural networks to solve inverse problems in localization and SLAM
(4) How to build the most efficient autonomous racecar with Multi-domain optimization across vehicle design, planning and control;

We realize all our research in the https://f1tenth.org autonomous racecar platform that is 10th the size, but 10x the fun! The main takeaway from this talk is how you can get involved in very exciting research on safe autonomous systems. I will also present projects on AV Gokart that we are doing in the Autoware Center of Excellence for Autonomous Driving.

Bio: Rahul builds safe autonomous systems at the intersection of formal methods, machine learning and controls. He applies his work to safety-critical autonomous vehicles, urban air mobility, life-critical medical devices, and AI Co-designers for complex systems. He is the Penn Director for the Department of Transportation’s $20MM Safety21 National UTC [2023-2028] which focuses on technologies for safe and efficient movement of people and goods. Rahul is the Director of the Autoware Center of Excellence for Autonomous Driving, a consortium of 70+ companies and universities focused on open-source AV software for open-standards EV platforms.

Rahul received the 2016 US Presidential Early Career Award (PECASE) from President Obama for his work on Life-Critical Systems. He also received the 2016 Department of Energy’s CleanTech Prize (Regional), the 2014 IEEE Benjamin Franklin Key Award, 2013 NSF CAREER Award, 2012 Intel Early Faculty Career Award and was selected by the National Academy of Engineering for the 2012 and 2017 US Frontiers of Engineering. He has won several ACM and IEEE best paper awards in Cyber-Physical Systems, controls, machine learning, and education.