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By learning visual representations from scene-level semantics down to pixel-level details, C2FMAE overcomes the limitations of both contrastive learning and masked image modeling.
Robots can now learn manipulation affordances from human demos with better contact pose accuracy, thanks to a new pipeline that automatically infers 3D pose-centered labels.
