  This work provides an architecture to enable robotic grasp planning via shape
completion. Shape completion is accomplished through the use of a 3D
convolutional neural network (CNN). The network is trained on our own new open
source dataset of over 440,000 3D exemplars captured from varying viewpoints.
At runtime, a 2.5D pointcloud captured from a single point of view is fed into
the CNN, which fills in the occluded regions of the scene, allowing grasps to
be planned and executed on the completed object. Runtime shape completion is
very rapid because most of the computational costs of shape completion are
borne during offline training. We explore how the quality of completions vary
based on several factors. These include whether or not the object being
completed existed in the training data and how many object models were used to
train the network. We also look at the ability of the network to generalize to
novel objects allowing the system to complete previously unseen objects at
runtime. Finally, experimentation is done both in simulation and on actual
robotic hardware to explore the relationship between completion quality and the
utility of the completed mesh model for grasping.
