AI 中文总结
该研究针对冻结6自由度抓取检测器提出GraRe抓取候选重排序方法，结合候选属性、局部几何与物体上下文，在GraspNet-1Billion实验中平均精度最高提升13.60点，真实机器人杂乱场景抓取表现鲁棒。
AI 中文摘要
现有6自由度抓取检测器通常依据检测器置信度对抓取候选进行排序。然而，我们对GraspNet-1Billion的分析表明，检测器置信度常与抓取质量匹配度较差，导致成功的抓取候选在执行过程中被排序过低。受此观察启发，我们将抓取候选重排序定义为冻结检测器的一项独立任务，旨在不改变检测器及其抓取候选的前提下优化候选排序。我们提出GraRe，其从候选属性、壳层分层局部几何以及物体上下文估计抓取质量。候选属性对局部几何与物体上下文表示进行条件约束，Transformer融合三类特征，预测质量与检测器置信度结合生成最终排序。在GraspNet-1Billion上针对三个冻结检测器的实验显示出一致的性能提升，平均精度最高提升13.60个百分点。真实机器人实验进一步验证了其在杂乱场景中的鲁棒抓取能力。这些结果表明，优化候选排序是增强冻结6自由度抓取检测器的实用方法。
英文摘要
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 quality is combined with detector confidence to produce the final ranking. Experiments on GraspNet-1Billion with five frozen detectors show consistent improvements, with gains of up to 13.56 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. Project code is available at https://minakanmi-yuki.github.io/grare/.
Comments23 pages, 34 figures. Supplementary material is included
