Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation
Peter Werner, Tim Seyde, Paul Drews, Thomas Matrai Balch, Igor Gilitschenski, Wilko Schwarting, Guy Rosman, Sertac Karaman
Abstract
Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free feedback control, multi-agent wheel-to-wheel racing poses several challenges including planning over unknown opponent intentions as well as negotiating interactions under dynamic constraints. We propose to address these challenges via a learning-based approach that effectively combines model-based techniques, massively 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. Further details and videos can be found on the project website: https://sites.google.com/view/dynmutr/home.
BibTeX
@inproceedings{
werner2023dynamic,
title={Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation},
author={Peter Werner and Tim Seyde and Paul Drews and Thomas Matrai Balch and Igor Gilitschenski and Wilko Schwarting and Guy Rosman and Sertac Karaman and Daniela Rus},
booktitle={7th Annual Conference on Robot Learning},
year={2023},
url={https://openreview.net/forum?id=fvXFBCHVGn}
}