  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.

Robotics;Computer Vision and Pattern Recognition