Invariant Representation Learning for Generalizable Imitation
Abstract
We address the problem of learning imitation policies that generalize across environments sharing the same underlying causal structure between the system dynamics and the task. We introduce a novel loss for learning invariant state representations that draws inspiration from adversarial robustness. Our approach is algorithm-agnostic and does not require knowledge of domain labels. Yet, evaluation in visual and non-visual environments reveals improved zero-shot generalization in the presence of spurious features compared to previous works.
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