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.

翻译：本文提出了一种名为双点网络(DD-Net)的反粒子抓取检测的新的深层次学习方法。它遵循了最近的无锚物体检测框架,这一框架并不依赖经验性预设锚,因此可以对不可见物体进行更加广泛和灵活的预测。具体地说,与广泛使用的五维矩形不同,抓取器配置被定义为指尖的一对。引入了一个有效的CNN架构,将指尖本地化,并在辅助中心的帮助下加以精细化,准确和有力地推断捕捉候选人。此外,我们设计了一个专门的丢失功能,以衡量抓取质量,与在目标检测中采用的IoU捆绑盒的分数相比,它更符合抓取检测任务。进行了模拟和机器人实验,并实现了艺术的进化状态,表明DD-Net比处理不可见物体的对应方优越。