GKNet: grasp keypoint network for grasp candidates detection
- URL: http://arxiv.org/abs/2106.08497v1
- Date: Wed, 16 Jun 2021 00:34:55 GMT
- Title: GKNet: grasp keypoint network for grasp candidates detection
- Authors: Ruinian Xu, Fu-Jen Chu and Patricio A. Vela
- Abstract summary: This paper presents a different approach to grasp detection by treating it as keypoint detection.
The deep network detects each grasp candidate as a pair of keypoints, convertible to the grasp representation g = x, y, w, thetaT, rather than a triplet or quartet of corner points.
- Score: 15.214390498300101
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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. The deep network detects each grasp candidate as a pair of
keypoints, convertible to the grasp representation g = {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 further
promote dependencies between keypoints, the general non-local module is
incorporated into the proposed learning framework. 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 the best balance of accuracy and speed on the
Cornell and the abridged Jacquard dataset (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 bin picking
experiments. The results confirm the hypothesis that grasp keypoints are an
effective output representation for deep grasp networks that provide robustness
to expected nuisance factors.
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