GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors
Existing 6-DoF grasp detectors typically rank grasp candidates by detector confidence. However, our analysis on GraspNet-1Billion shows that detector confidence is often poorly aligned with grasp quality, leaving successful grasp candidates at low ranks. Motivated by this observation, we study whether learned re-ranking can improve candidate ordering while keeping detector parameters and grasp candidates unchanged. We propose GraRe, which estimates grasp quality from candidate attributes, shell-stratified local geometry, and object context. Candidate attributes condition the local geometric and object-context representations, and a Transformer fuses all three feature types. The predicted ...
| Authors | Jibao Yuan, Yuhui Zhao, Yinzhen Lv et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 August 2026 |
| Last updated at source | 3 October 2026 |
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