SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation

arXiv:2310.16838v2 Announce Type: replace-cross
Abstract: Humans demonstrate remarkable skill in transferring manipulation abilities across objects of varying shapes, poses, and appearances, a capability rooted in their understanding of semantic correspondences between different instances. To equip robots with a similar high-level comprehension, we present SparseDFF, a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic features from sparse RGBD images, a domain where research is limited despite its relevance to many tasks with fixed-camera setups. SparseDFF generates …

3d scenes abstract arxiv capability cs.cv cs.ro distillation feature humans instances manipulation novel objects robots semantic type understanding view