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.

中文翻译：

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用于对足抓取检测的双点网络

本文提出了一种新的对足抓取检测深度学习方法，称为双点网络（DD-Net）。它遵循最近的无锚物体检测框架，它不依赖于经验预设的锚点，因此允许对看不见的物体进行更广泛和灵活的预测。具体来说，与广泛使用的 5 维矩形不同，夹持器配置被定义为一对指尖。引入了一种有效的 CNN 架构来定位这些指尖，并在辅助中心的帮助下进行细化，它可以准确而稳健地推断出抓取候选对象。此外，我们设计了一个专门的损失函数来衡量抓取的质量，与目标检测中采用的边界框的 IoU 分数相比，它更符合抓取检测任务。