低コストセンサのみで実現するアジャイルなオフロード自律走行のエンドツーエンド模倣学習
Agile Autonomous Driving using End-to-End Deep Imitation Learning
高度なセンサを備えたモデル予測制御器の挙動を深層ニューラルネットで模倣し、低コストセンサの生観測から直接ステアリングとスロットルを出力する自律走行システムを構築、オンライン模倣学習が分布シフトに強く高速オフロード走行で最先端性能を達成した。
著者: Yunpeng Pan, Ching-An Cheng, Kamil Saigol, Keuntaek Lee, Xinyan Yan, Evangelos Theodorou, Byron Boots
分類: cs.RO
原文アブストラクト
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost 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.
