Contemporary grasp detection approaches employ deep learning to achieve robustness to sensor and object model uncertainty. The two dominant approaches design either grasp-quality scoring or anchor-based grasp recognition networks. This paper presents a different approach to grasp detection by treating it as keypoint detection in image-space. The deep network detects each grasp candidate as a pair of keypoints, convertible to the grasp representationg = {x, y, w, {\theta}} T , rather than a triplet or quartet of corner points. Decreasing the detection difficulty by grouping keypoints into pairs boosts performance. To promote capturing dependencies between keypoints, a non-local module is incorporated into the network design. A final filtering strategy based on discrete and continuous orientation prediction removes false correspondences and further improves grasp detection performance. GKNet, the approach presented here, achieves a good balance between accuracy and speed on the Cornell and the abridged Jacquard datasets (96.9% and 98.39% at 41.67 and 23.26 fps). Follow-up experiments on a manipulator evaluate GKNet using 4 types of grasping experiments reflecting different nuisance sources: static grasping, dynamic grasping, grasping at varied camera angles, and bin picking. GKNet outperforms reference baselines in static and dynamic grasping experiments while showing robustness to varied camera viewpoints and moderate clutter. The results confirm the hypothesis that grasp keypoints are an effective output representation for deep grasp networks that provide robustness to expected nuisance factors.

翻译：当代掌握探测方法采用深层次的学习方法,使感官和对象模型的不确定性达到稳健性。 两种主要方法要么是用掌握质量的评分或以锚为基础的抓取识别网 。 本文展示了一种不同的方法, 将检测作为图像空间中的关键点检测来进行。 深层次的网络将每个抓取候选人都检测成一对关键点, 可以转换到 = {x, y, w, {theta ⁇ T, 而不是角点的三重制或四重四重制。 通过将键点组合成对配方来降低检测难度, 提高性能。 为了在网络设计中捕捉关键点之间的依赖性, 将非本地模块纳入网络设计中。 基于离散和连续定向预测的最终过滤战略, 消除虚假的对应性通信, 进一步提高探测性绩效。 GKNet 此处介绍的方法在康奈尔 和缩略式的静态数据基点( 96.9和98.39 % 和23.26 fps) 。 跟踪实验中, 显示操纵和精确度的准确性精确度的准确性精确性精确度, 和精确度的精确度的精确度, 正在显示, 展示的精确度的精确度的精确度, 和精确度的精确度的对比的对比的精确度, 和精确度是在网络的参照值的对比, 。