GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors

arXiv:2608.00946 · cs.RO, cs.AI, cs.CV, cs.LG · Submitted 2026-08-02 · Read on arXiv

cs.RO, cs.AI, cs.CV, cs.LG

Submitted: 2026-08-02

Updated: 2026-10-03

Comments: 23 pages, 34 figures. Supplementary material is included

Code: https://github.com/Minakanmi-Yuki/grare

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 11× in this paper · explained in 2 episodes
• Transformer — 11× in this paper · explained in 2 episodes
• Ablation — 8× in this paper · explained in 1 episode
• Trace — 7× in this paper · explained in 2 episodes
• Point Clouds — 5× in this paper · explained in 2 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Affordance — 1× in this paper · explained in 1 episode
• Average Precision — 1× in this paper
• CVF International Conference on Computer Vision — 1× in this paper
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Grasp the Graph — 1× in this paper
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Mask Generation — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes

Sources

• Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter
• GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes
• Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

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