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Beyond State-as-Action: Exploiting Command-State Discrepancy for Robot Imitation Learning
Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may reflect control demands that motion alone does not capture. We investigate when this information matters and how to exploit i
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