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Autonomous racing with scaled race cars has gained increasing attention as an
effective approach for developing perception, planning and control algorithms
for safe autonomous driving at the limits of the vehicle's handling. To train
agile control policies for autonomous racing, learning-based approaches largely
utilize reinforcement learning, albeit with mixed results. In this study, we
benchmark a variety of imitation learning policies for racing vehicles that are
applied directly or for bootstrapping reinforcement learning both in simulation
and on scaled real-world environments. We show that interactive imitation
learning techniques outperform traditional imitation learning methods and can
greatly improve the performance of reinforcement learning policies by
bootstrapping thanks to its better sample efficiency. Our benchmarks provide a
foundation for future research on autonomous racing using Imitation Learning
and Reinforcement Learning.
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