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We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors. By imitating a model predictive controller equipped with advanced sensors, we train a deep neural network control policy to map raw, high-dimensional observations to continuous steering and throttle commands. Compared with recent approaches to similar tasks, our method requires neither state estimation nor on-the-fly planning to navigate the vehicle. Our approachdoi:10.15607/rss.2018.xiv.056 dblp:conf/rss/PanCSLYTB18 fatcat:p3wthkfno5gdrammgarr32ujbq