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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.<\/jats:p>","DOI":"10.1177\/0278364919880273","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T06:38:34Z","timestamp":1571035114000},"page":"286-302","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":111,"title":["Imitation learning for agile autonomous driving"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3373-0769","authenticated-orcid":false,"given":"Yunpeng","family":"Pan","sequence":"first","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Ching-An","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute for Robotics and Intelligent Machines, Georgia Institute of Technology, Atlanta, 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