arXiv CS
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EgoLAP: Learning from Egocentric Human Data through Language-Action Reasoning
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for control. Our key insight is that, although low-level actions are embodiment-specific, their underlying motion intent can
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