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

2026-08-01

AI Summary

This work addresses the misalignment between the confidence scores produced by existing 6-DoF grasp detectors and actual grasp quality, which often leads to high-quality candidates being underestimated. To resolve this, the paper introduces a novel grasp re-ranking module decoupled from the detector, enabling refinement without modifying the frozen detector. By integrating candidate attributes, shell-wise hierarchical local geometry, and object-level contextual information through a conditional representation and a Transformer-based multimodal fusion strategy, the method predicts more accurate grasp quality scores for improved ranking. Evaluated on GraspNet-1Billion, the approach boosts the average AP of three frozen detectors by up to 13.60 points. Real-world robotic experiments further demonstrate its robust performance in cluttered scenes.

Abstract

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, causing successful grasp candidates to be ranked too low during execution. Motivated by this observation, we formulate grasp candidate re-ranking as a separate task for frozen detectors, aiming to improve candidate ordering without changing the detector or its grasp candidates. 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 quality is combined with detector confidence to produce the final ranking. Experiments on GraspNet-1Billion with three frozen detectors show consistent improvements, with gains of up to 13.60 points in Average AP. Real-robot experiments further demonstrate robust grasping in cluttered scenes. These results show that improving candidate ranking provides a practical way to enhance frozen 6-DoF grasp detectors.

Problem

• 6-DoF grasp detection
• grasp candidate ranking
• grasp quality
• frozen detectors

Innovation

• grasp re-ranking
• frozen grasp detector
• 6-DoF grasping
• Transformer-based fusion
• grasp quality estimation