  Animals are able to imitate each others' behavior, despite their difference
in biomechanics. In contrast, imitating the other similar robots is a much more
challenging task in robotics. This problem is called cross domain imitation
learning~(CDIL). In this paper, we consider CDIL on a class of similar robots.
We tackle this problem by introducing an imitation learning algorithm based on
invariant representation. We propose to learn invariant state and action
representations, which aligns the behavior of multiple robots so that CDIL
becomes possible. Compared with previous invariant representation learning
methods for similar purpose, our method does not require human-labeled pairwise
data for training. Instead, we use cycle-consistency and domain confusion to
align the representation and increase its robustness. We test the algorithm on
multiple robots in simulator and show that unseen new robot instances can be
trained with existing expert demonstrations successfully. Qualitative results
also demonstrate that the proposed method is able to learn similar
representations for different robots with similar behaviors, which is essential
for successful CDIL.
