Skeleton-guided geometry-aware auto-labeling for robotic grasping: From 4-DoF learning to 6-DoF execution
Skeleton-guided geometry-aware auto-labeling for robotic grasping: From 4-DoF learning to 6-DoF execution
This research introduces a novel algorithm for generating grasp keypoints using straight skeletons to identify high-potential regions for stable grasping. A fully automated pipeline enables large-scale annotation without human intervention, and a new architecture, SkelGG-CNN, is introduced for 4-DoF grasping. The method is extended to 6-DoF grasping using a generalized quasi-3D skeletonization algorithm, achieving high accuracy on multiple benchmarks and real-world experiments. The framework enables efficient training of lightweight models for real-world deployment.
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This paper presents a novel approach to robotic grasping by introducing a skeleton-guided auto-labeling method that reduces the need for manual annotation. The SkelGG-CNN architecture demonstrates strong performance across multiple benchmarks, with high accuracy in both simulation and real-world settings, suggesting potential for scalable and efficient training of grasping models.
Key findings, evidence and limits
Published automatically after robotics and source-evidence checks; no manual editorial approval is recorded. Source assertions are not independently verified. Missing information remains not reported.
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| Metric | Value / unit | Basis / context | Evidence |
|---|---|---|---|
| accuracy | 95.42 percent | Basis not reported Source wording: “95.42%, 96.55%, and 97.1% accuracy on Jacquard V1, Jacquard V2, and Cornell benchmarks” | Source E1 |
| average precision | 18.33 percent | Basis not reported Source wording: “an average precision of 18.33 on the novel set” | Source E1 |
| success rate | 98.4 percent | Basis not reported Source wording: “success rates of 98.4% on the Dex-Net test set” | Source E1 |
| success rate | 89.1 percent | Basis not reported Source wording: “89.1% on EGAD!” | Source E1 |
| success rate | 97.4 percent | Basis not reported Source wording: “97.4% success on single household objects” | Source E1 |
| success rate | 95 percent | Basis not reported Source wording: “95.0% in cluttered scenes” | Source E1 |
- dataset: Not reported
- code: Available Artifact linkSource E1
Source excerpts and review record
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Original source quotation: “Evaluations demonstrate that SkelGG-CNN achieves 95.42%, 96.55%, and 97.1% accuracy on Jacquard V1, Jacquard V2, and Cornell benchmarks, respectively, using 4-DoF auto-generated labels.”
Source E1
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Source：International Journal of Robotics Research — Journal metadata · doi.org