Agile Autonomous Driving using End-to-End Deep Imitation Learning
Agile Autonomous Driving using End-to-End Deep Imitation Learning
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DOI：
10.15607/rss.2018.xiv.056
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Yunpeng Pan;Ching-An Cheng;Kamil Saigol;Keuntaek Lee;Xinyan Yan;Evangelos A. Theodorou;Byron Boots
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文献类型：
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作者：
Yunpeng Pan;Ching-An Cheng;Kamil Saigol;Keuntaek Lee;Xinyan Yan;Evangelos A. Theodorou;Byron Boots
我们提出了一个端到端的模仿学习系统，只使用低成本的传感器进行敏捷的越野自动驾驶。通过模仿配备先进传感器的模型预测控制器，我们训练了一个深度神经网络控制策略，将原始的高维观测映射到连续的转向和油门命令。与最近的类似任务的方法相比，我们的方法既不需要状态估计，也不需要飞行规划来导航车辆。我们的方法依赖于，并通过实验验证，最近的模仿学习理论。从经验上讲，我们表明，用在线模仿学习训练的政策克服了与协变量转移相关的众所周知的挑战，并且比用批量模仿学习训练的政策更好地推广。基于这些见解，我们的自动驾驶系统展示了成功的高速越野驾驶，与最先进的性能相匹配。
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.