Uploaded December 2021 | Updated September 2026, 32 minutes ago
ONLINE Lockheed Martin Robotics Seminar: What can we learn from Autonomous Racing?
Rahul Mangharam
Associate Professor
Department of Electrical and Systems Engineering
University of Pennsylvania
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 build the most efficient autonomous racecar with Multi- domain optimization across vehicle design, planning and control; (2) How to generate the most competitive agents who dynamically balance safety and assertiveness by using distributionally robust online adaptation; (3) How to stress test the overtaking logic and path planning algorithms in interactive adversarial agents; (4) How to combine previous system designs to auto-complete new designs with new requirements, and (5) Understand the value of Cooperation in Multi-Agent Games. We realize all our research in the f1tenth.org autonomous racecar platform that is 10 th the size, but 10x the fun! The main take away from this talk is how you can get involved in very exciting research on safe autonomous systems. This a team presentation by Rahul Mangharam, Johannes Betz and Billy Zheng.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
ONLINE Lockheed Martin Robotics Seminar: What can we learn from Autonomous Racing?
Rahul Mangharam
Associate Professor
Department of Electrical and Systems Engineering
University of Pennsylvania
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 build the most efficient autonomous racecar with Multi- domain optimization across vehicle design, planning and control; (2) How to generate the most competitive agents who dynamically balance safety and assertiveness by using distributionally robust online adaptation; (3) How to stress test the overtaking logic and path planning algorithms in interactive adversarial agents; (4) How to combine previous system designs to auto-complete new designs with new requirements, and (5) Understand the value of Cooperation in Multi-Agent Games. We realize all our research in the f1tenth.org autonomous racecar platform that is 10 th the size, but 10x the fun! The main take away from this talk is how you can get involved in very exciting research on safe autonomous systems. This a team presentation by Rahul Mangharam, Johannes Betz and Billy Zheng.
For more information on the Maryland Robotics Center see:
https://robotics.umd.edu
