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LM Robotics Seminar: What can we learn from Autonomous Racing?
What can we learn from Autonomous Racing?
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 https://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.
Rahul's 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, IoT4Agriculture, and AI Co-designers for complex systems. He is the Penn Director for the Department of Transportation's $14MM Mobility21 National University Transportation Center which focuses on technologies for safe and efficient movement of people and goods. 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. Rahul is an Associate Professor in the Dept. of Electrical & Systems Engineering and Dept. of Computer & Information Science at the University of Pennsylvania. He received his Ph.D. in Electrical & Computer Engineering from Carnegie Mellon University. He enjoys organizing autonomous racing competitions at https://f1tenth.org