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HARP-VLA: Human-Robot Aligned Representation Learning for Vision-Language-Action Model
Learning generalizable vision-language-action (VLA) models from large-scale human videos is promising but challenging due to cross-embodiment discrepancies in both visual observations and executable actions. While latent action models reduce the action execution gap by learning action abstractions,
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