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Contemporary grasp detection approaches employ deep learning to achieverobustness to sensor and object model uncertainty. The two dominant approachesdesign either grasp-quality scoring or anchor-based grasp recognition networks.This paper presents a different approach to grasp detection by treating it askeypoint detection in image-space. The deep network detects each graspcandidate 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 boostsperformance. To promote capturing dependencies between keypoints, a non-localmodule is incorporated into the network design. A final filtering strategybased on discrete and continuous orientation prediction removes falsecorrespondences and further improves grasp detection performance. GKNet, theapproach presented here, achieves a good balance between accuracy and speed onthe Cornell and the abridged Jacquard datasets (96.9% and 98.39% at 41.67 and23.26 fps). Follow-up experiments on a manipulator evaluate GKNet using 4 typesof grasping experiments reflecting different nuisance sources: static grasping,dynamic grasping, grasping at varied camera angles, and bin picking. GKNetoutperforms reference baselines in static and dynamic grasping experimentswhile showing robustness to varied camera viewpoints and moderate clutter. Theresults confirm the hypothesis that grasp keypoints are an effective outputrepresentation for deep grasp networks that provide robustness to expectednuisance factors.

GKNet: grasp keypoint network for grasp candidates detection