  In this paper, we present a novel deep neural network architecture for joint
class-agnostic object segmentation and grasp detection for robotic picking
tasks using a parallel-plate gripper. We introduce depth-aware Coordinate
Convolution (CoordConv), a method to increase accuracy for point proposal based
object instance segmentation in complex scenes without adding any additional
network parameters or computation complexity. Depth-aware CoordConv uses depth
data to extract prior information about the location of an object to achieve
highly accurate object instance segmentation. These resulting segmentation
masks, combined with predicted grasp candidates, lead to a complete scene
description for grasping using a parallel-plate gripper. We evaluate the
accuracy of grasp detection and instance segmentation on challenging robotic
picking datasets, namely Sil\'eane and OCID_grasp, and show the benefit of
joint grasp detection and segmentation on a real-world robotic picking task.
