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arXiv · 1812.03823

Learning to Drive from Simulation without Real World Labels

Alex Bewley·Jessica Rigley·Yuxuan Liu·Jeffrey Hawke·Richard Shen·Vinh-Dieu Lam·Alex Kendall
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Abstract

Simulation can be a powerful tool for understanding machine learning systems and designing methods to solve real-world problems. Training and evaluating methods purely in simulation is often "doomed to succeed" at the desired task in a simulated environment, but the resulting models are incapable of operation in the real world. Here we present and evaluate a method for transferring a vision-based lane following driving policy from simulation to operation on a rural road without any real-world labels. Our approach leverages recent advances in image-to-image translation to achieve domain transfer while jointly learning a single-camera control policy from simulation control labels. We assess the driving performance of this method using both open-loop regression metrics, and closed-loop performance operating an autonomous vehicle on rural and urban roads.

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Alex Bewley, Jessica Rigley, Yuxuan Liu, Jeffrey Hawke, Richard Shen, Vinh-Dieu Lam, Alex Kendall. 2018-12-13. Learning to Drive from Simulation without Real World Labels. https://arxiv.org/abs/1812.03823

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