  Classically, imitation learning algorithms have been developed for idealized
situations, e.g., the demonstrations are often required to be collected in the
exact same environment and usually include the demonstrator's actions.
Recently, however, the research community has begun to address some of these
shortcomings by offering algorithmic solutions that enable imitation learning
from observation (IfO), e.g., learning to perform a task from visual
demonstrations that may be in a different environment and do not include
actions. Motivated by the fact that agents often also have access to their own
internal states (i.e., proprioception), we propose and study an IfO algorithm
that leverages this information in the policy learning process. The proposed
architecture learns policies over proprioceptive state representations and
compares the resulting trajectories visually to the demonstration data. We
experimentally test the proposed technique on several MuJoCo domains and show
that it outperforms other imitation from observation algorithms by a large
margin.
