The goal of imitation learning is to mimic expert behavior without access to
an explicit reward signal. Expert demonstrations provided by humans, however,
often show significant variability due to latent factors that are typically not
explicitly modeled. In this paper, we propose a new algorithm that can infer
the latent structure of expert demonstrations in an unsupervised way. Our
method, built on top of Generative Adversarial Imitation Learning, can not only
imitate complex behaviors, but also learn interpretable and meaningful
representations of complex behavioral data, including visual demonstrations. In
the driving domain, we show that a model learned from human demonstrations is
able to both accurately reproduce a variety of behaviors and accurately
anticipate human actions using raw visual inputs. Compared with various
baselines, our method can better capture the latent structure underlying expert
demonstrations, often recovering semantically meaningful factors of variation
in the data.

本文提出了一种基于对抗生成模型的模仿学习算法，能够通过无监督学习方法推断出专家示范中隐藏的潜在结构，并可以学习到复杂行为数据可解释且有意义的表示方式，包括图像示范。在驾驶领域中，我们展示了通过人类示范学习的模型能够准确地复现多种行为并能使用原始视觉输入准确地预测人类的行为。相比于其他基线算法，我们的方法能更好地捕捉隐藏在专家示范中的潜在结构，并经常回收到语义上有意义的数据变量。