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Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
[article]
2020
arXiv
pre-print
Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects from partial object views. However, existing approaches lack the ability to explicitly reason about the full 3D geometry of the object when selecting a grasp, relying on indirect geometric reasoning derived when learning grasp success networks. This abandons explicit geometric reasoning, such as avoiding undesired robot object collisions. We propose to utilize a novel, learned 3D reconstruction to
arXiv:1910.00983v2
fatcat:i5knixbuczgmzkm74p6ofl5n2i