  Grasping is a fundamental robotic task needed for the deployment of household
robots or furthering warehouse automation. However, few approaches are able to
perform grasp detection in real time (frame rate). To this effect, we present
Grasp Quality Spatial Transformer Network (GQ-STN), a one-shot grasp detection
network. Being based on the Spatial Transformer Network (STN), it produces not
only a grasp configuration, but also directly outputs a depth image centered at
this configuration. By connecting our architecture to an externally-trained
grasp robustness evaluation network, we can train efficiently to satisfy a
robustness metric via the backpropagation of the gradient emanating from the
evaluation network. This removes the difficulty of training detection networks
on sparsely annotated databases, a common issue in grasping. We further propose
to use this robustness classifier to compare approaches, being more reliable
than the traditional rectangle metric. Our GQ-STN is able to detect robust
grasps on the depth images of the Dex-Net 2.0 dataset with 92.4 % accuracy in a
single pass of the network. We finally demonstrate in a physical benchmark that
our method can propose robust grasps more often than previous sampling-based
methods, while being more than 60 times faster.
