Skeleton-Guided Geometry-Aware Auto-Labeling for Robotic Grasping: From 4-DoF to
Overview
This research presents a skeleton-based algorithm for generating grasp keypoints and identifying stable grasp regions for robotic grasping. The proposed SkelGG-CNN model achieves high accuracy in both 4-DoF and 6-DoF grasping tasks, with performance comparable to state-of-the-art methods. The approach enables efficient training of lightweight models on small datasets and provides scalable annotations for cluttered 3D scenes.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Environment: simulation
“nd Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming sta”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 98.4 percent · Basis not reported · Differing source assertions
“nd Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming sta”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 89.1 percent · Basis not reported · Differing source assertions
“nd Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming sta”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 97.4 percent · Basis not reported · Differing source assertions
“nd Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming sta”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 95 percent · Basis not reported · Differing source assertions
“nd Cornell, respectively. On the GraspNet novel split, 6-DoF auto-labeling achieves an average precision of 18.33, comparable to state-of-the-art model-based approaches. In simulation, the 6-DoF method achieves success rates of 98.4% on Dex-Net and 89.1% on EGAD!. Real-world experiments with a Delta parallel robot show success rates of 97.4% on single household objects and 95.0% in cluttered scenes, outperforming sta”
Exact source · revision 1Source owner not reported
Evidence dependency changes (3)
- 2026-10-30T18:06:02.244Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T17:37:36.494Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T17:12:00.801Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalSkeleton-Guided Geometry-Aware Auto-Labeling for Robotic Grasping: From 4-DoF Learning to 6-DoF Execution
This research presents a skeleton-based algorithm for generating grasp keypoints and identifying stable grasp regions for robotic grasping. The proposed SkelGG-CNN model achieves high accuracy in both 4-DoF and 6-DoF grasping tasks, with performance comparable to state-of-the-art methods. The approach enables efficient training of lightweight models on small datasets and provides scalable annotations for cluttered 3D scenes.
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