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Results show that robust visuomotor learning benefits from structuring, rather than removing, visual embodiment information, and embodiment canonicalization in 3D point clouds substantially improves human-to-robot policy transfer without robot demonstrations.
Flow-Map Distillation achieves superior image restoration by transforming static knowledge transfer into a dynamic flow mapping process, cutting training variance in half.
