How do continuous latent action representations compare to discrete token methods in terms of sample efficienc

Abstract

Abstract: Learning robot policies using imitation learning requires collecting large amounts of costly action-labeled expert demonstrations, which fundamentally limits the scale of training data. A promising approach to address this bottleneck is to harness the abundance of unlabeled observations-e.g., from video demonstrations-to learn latent action labels in an unsupervised way. However, we find that existing methods struggle when applied to complex robot tasks requiring fine-grained motions. We design continuous latent action models (CLAM) which incorporate two key ingredients we find necessary for l

Research Question

How do continuous latent action representations compare to discrete token methods in terms of sample efficiency when training robot policies from video demonstrations on complex manipulation benchmarks like MetaWorld?

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