We study the question of how to imitate tasks across domains with
discrepancies such as embodiment, viewpoint, and dynamics mismatch. Many prior
works require paired, aligned demonstrations and an additional RL step that
requires environment interactions. However, paired, aligned demonstrations are
seldom obtainable and RL procedures are expensive. We formalize the Domain
Adaptive Imitation Learning (DAIL) problem, which is a unified framework for
imitation learning in the presence of viewpoint, embodiment, and dynamics
mismatch. Informally, DAIL is the process of learning how to perform a task
optimally, given demonstrations of the task in a distinct domain. We propose a
two step approach to DAIL: alignment followed by adaptation. In the alignment
step we execute a novel unsupervised MDP alignment algorithm, Generative
Adversarial MDP Alignment (GAMA), to learn state and action correspondences
from \emph{unpaired, unaligned} demonstrations. In the adaptation step we
leverage the correspondences to zero-shot imitate tasks across domains. To
describe when DAIL is feasible via alignment and adaptation, we introduce a
theory of MDP alignability. We experimentally evaluate GAMA against baselines
in embodiment, viewpoint, and dynamics mismatch scenarios where aligned
demonstrations don't exist and show the effectiveness of our approach.

介绍了一种领域自适应模仿学习（DAIL）问题的形式化表达，提出了一种两步方法：对齐和适应，通过无监督的 MDP 对齐算法（GAMA）执行对齐步骤并利用对应关系在不需要经过环境交互的情况下在不同领域中实现任务零次模仿。