We develop an image-based sim-to-real transfer technique for deep reinforcement learning. First, we train a teacher model to move along a near optimal path. We then use this model to teach a student model the correct actions along with randomization. The technique bridges the sim-to-real gap, improving the driving speedrobustness of the simulator-trained student model in the real world.

The sim-to-real transfer technique we proposed can bridge the sim-to-real gap in miniature autonomous car racing. The breakthrough is that we can boost the robustness of a simulator-trained model without compromising racing lap times in the real world. We published the research result in IROS 2020 (Workshop)achieved great success in AWS DeepRacer races.

This technique bridges the sim-to-real gapincreases the feasibility of using deep reinforcement learning for real-world applications. The autonomous driving technique can be applied to racing cars in the International Federation of Model Auto Racing (IFMAR) to make the competition more attractive. Besides, it can be applied to automated guided vehicles (AGV) in the production line for the low costrapid deployment. Moreover, it can be applied to the rescueexploration missions in high-risk environments such as rough terrain, to improve the efficiencythe region of rescue.

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