  Deep learning has been widely used for inferring robust grasps. Although
human-labeled RGB-D datasets were initially used to learn grasp configurations,
preparation of this kind of large dataset is expensive. To address this
problem, images were generated by a physical simulator, and a physically
inspired model (e.g., a contact model between a suction vacuum cup and object)
was used as a grasp quality evaluation metric to annotate the synthesized
images. However, this kind of contact model is complicated and requires
parameter identification by experiments to ensure real world performance. In
addition, previous studies have not considered manipulator reachability such as
when a grasp configuration with high grasp quality is unable to reach the
target due to collisions or the physical limitations of the robot. In this
study, we propose an intuitive geometric analytic-based grasp quality
evaluation metric. We further incorporate a reachability evaluation metric. We
annotate the pixel-wise grasp quality and reachability by the proposed
evaluation metric on synthesized images in a simulator to train an
auto-encoder--decoder called suction graspability U-Net++ (SG-U-Net++).
Experiment results show that our intuitive grasp quality evaluation metric is
competitive with a physically-inspired metric. Learning the reachability helps
to reduce motion planning computation time by removing obviously unreachable
candidates. The system achieves an overall picking speed of 560 PPH (pieces per
hour).
