  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 enable geometric awareness in a
grasping system. We leverage the structure of the reconstruction network to
learn a grasp success classifier which serves as the objective function for a
continuous grasp optimization. We additionally explicitly constrain the
optimization to avoid undesired contact, directly using the reconstruction. We
examine the role of geometry in grasping both in the training of grasp metrics
and through 96 robot grasping trials. Our results can be found on
https://sites.google.com/view/reconstruction-grasp/.
