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Classically, imitation learning algorithms have been developed for idealizedsituations, e.g., the demonstrations are often required to be collected in theexact same environment and usually include the demonstrator's actions.Recently, however, the research community has begun to address some of theseshortcomings by offering algorithmic solutions that enable imitation learningfrom observation (IfO), e.g., learning to perform a task from visualdemonstrations that may be in a different environment and do not includeactions. Motivated by the fact that agents often also have access to their owninternal states (i.e., proprioception), we propose and study an IfO algorithmthat leverages this information in the policy learning process. The proposedarchitecture learns policies over proprioceptive state representations andcompares the resulting trajectories visually to the demonstration data. Weexperimentally test the proposed technique on several MuJoCo domains and showthat it outperforms other imitation from observation algorithms by a largemargin.

Imitation Learning from Video by Leveraging Proprioception