  Grasping objects is one of the most important abilities that a robot needs to
master in order to interact with its environment. Current state-of-the-art
methods rely on deep neural networks trained to jointly predict a graspability
score together with a regression of an offset with respect to grasp reference
parameters. However, these two predictions are performed independently, which
can lead to a decrease in the actual graspability score when applying the
predicted offset. Therefore, in this paper, we extend a state-of-the-art neural
network with a scorer that evaluates the graspability of a given position, and
introduce a novel loss function which correlates regression of grasp parameters
with graspability score. We show that this novel architecture improves
performance from 82.13% for a state-of-the-art grasp detection network to
85.74% on Jacquard dataset. When the learned model is transferred onto a real
robot, the proposed method correlating graspability and grasp regression
achieves a 92.4% rate compared to 88.1% for the baseline trained without the
correlation.
