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• W2758920802 abstract "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 approach relies on, and experimentally validates, recent imitation learning theory. Empirically, we show that policies trained with online imitation learning overcome well-known challenges related to covariate shift and generalize better than policies trained with batch imitation learning. Built on these insights, our autonomous driving system demonstrates successful high-speed off-road driving, matching the state-of-the-art performance." @default.
• W2758920802 created "2017-10-06" @default.
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• W2758920802 date "2017-09-21" @default.
• W2758920802 modified "2023-09-26" @default.
• W2758920802 title "Agile Off-Road Autonomous Driving Using End-to-End Deep Imitation Learning." @default.
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