  This paper proposes a new deep learning approach to antipodal grasp
detection, named Double-Dot Network (DD-Net). It follows the recent anchor-free
object detection framework, which does not depend on empirically pre-set
anchors and thus allows more generalized and flexible prediction on unseen
objects. Specifically, unlike the widely used 5-dimensional rectangle, the
gripper configuration is defined as a pair of fingertips. An effective CNN
architecture is introduced to localize such fingertips, and with the help of
auxiliary centers for refinement, it accurately and robustly infers grasp
candidates. Additionally, we design a specialized loss function to measure the
quality of grasps, and in contrast to the IoU scores of bounding boxes adopted
in object detection, it is more consistent to the grasp detection task. Both
the simulation and robotic experiments are executed and state of the art
accuracies are achieved, showing that DD-Net is superior to the counterparts in
handling unseen objects.
