Natural imitation in humans usually consists of mimicking visual demonstrations of another person by continuously refining our skills until our performance is visually akin to the expert demonstrations. In this paper, we are interested in imitation learning of artificial agents in the natural setting - acquiring motor skills by watching raw video demonstrations. Traditional methods for learning from videos rely on extracting meaningful low-dimensional features from the videos followed by a separate hand-crafted reward estimation step based on feature separation between the agent and expert. We propose an imitation learning framework from raw video demonstrations, that reduces the dependence on hand engineered reward functions, by jointly learning the feature extraction and separation estimation steps, using generative adversarial networks. Additionally, we establish the equivalence between adversarial imitation from image manifolds and low-level state distribution matching, under certain conditions. Experimental results show that our proposed imitation learning method from raw videos produces a similar performance to state-of-the-art imitation learning techniques with low-level state and action information available while outperforming existing video imitation methods. Furthermore, we show that our method can learn action policies by imitating video demonstrations available on YouTube with performance comparable to learned agents from true reward signal. Please see the video at https://youtu.be/bvNpV2Q4rOA.

为了更好地控制自然动作，本文提出了一种深度学习方法，通过直接模仿原始视频演示来获得控制策略，并且使用生成对抗网络依据低维度特征代替手工奖励估计步骤，同时展示了该方法可以产生类似于专家演示视频的学习性能。

注射式状态-图像映射用于视觉对抗性模仿学习