How does CLAM's robustness compare to discrete token methods when evaluated on the Calibrated Action Benchmark

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 does CLAM's robustness compare to discrete token methods when evaluated on the Calibrated Action Benchmark (CAB) with varying degrees of out-of-distribution noise in video data?

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