  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.
