Imitation learning (IL) has achieved considerable success in solving complex
sequential decision-making problems. However, current IL methods mainly assume
that the environment for learning policies is the same as the environment for
collecting expert datasets. Therefore, these methods may fail to work when
there are slight differences between the learning and expert environments,
especially for challenging problems with high-dimensional image observations.
However, in real-world scenarios, it is rare to have the chance to collect
expert trajectories precisely in the target learning environment. To address
this challenge, we propose a novel robust imitation learning approach, where we
develop an inverse dynamics state representation learning objective to align
the expert environment and the learning environment. With the abstract state
representation, we design an effective reward function, which thoroughly
measures the similarity between behavior data and expert data not only
element-wise, but also from the trajectory level. We conduct extensive
experiments to evaluate the proposed approach under various visual
perturbations and in diverse visual control tasks. Our approach can achieve a
near-expert performance in most environments, and significantly outperforms the
state-of-the-art visual IL methods and robust IL methods.

我们提出了一种新颖的鲁棒模仿学习方法，通过发展逆动力学状态表示学习目标来对齐专家环境和学习环境，通过设计有效的奖励函数来度量行为数据和专家数据之间的相似性，从而在各种视觉扰动和视觉控制任务中实现近乎专家的性能，并显著优于当前最先进的视觉模仿学习方法和鲁棒模仿学习方法。