Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation


Autonomous racing is a challenging task that requires vehicle han- dling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free feed- back control, multi-agent wheel-to-wheel racing poses several challenges includ- ing planning over unknown opponent intentions as well as negotiating interac- tions under dynamic constraints. We propose to address these challenges via a learning-based approach that effectively combines model-based techniques, mas- sively parallel simulation, and self-play reinforcement learning to enable zero- shot sim-to-real transfer of highly dynamic policies. We deploy our algorithm in wheel-to-wheel multi-agent races on scale hardware to demonstrate the efficacy of our approach.

Conference on Robot Learning (CoRL)

Toronto Intelligent Systems Lab Co-authors

Igor Gilitschenski
Igor Gilitschenski
Assistant Professor