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.
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
accuracy
95.42%
View original evidence
95.42%, 96.55%, and 97.1% accuracy on Jacquard V1, Jacquard V2, and Cornell benchmarks
Open source E1
- dataset: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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.
Open source E1
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Source：International Journal of Robotics Research — Journal metadata · doi.org