发表机构
Sungkyunkwan University（成均馆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EquiGQNet通过SO(3)-等变共享编码和中间动作融合，高效评估6-DoF抓取质量，在仿真和真实场景中实现高成功率并大幅加速规划。
AI 中文摘要
从单视角深度图像中为杂乱桌面场景中的未见物体规划六自由度（6-DoF）抓取，需要对多样化的抓取候选进行准确且高效的评估。现有的早期融合方法捕获每个抓取候选相对的局部物体几何，但会重复编码场景，而晚期融合方法重用共享的场景表示，但可能丢失这种抓取相对的局部几何。我们提出EquiGQNet，一种高效的6-DoF抓取质量评估器，结合了两种方法的优点。对于抓取方向，EquiGQNet将早期融合中为每个抓取候选旋转并重新编码点云的操作替换为SO(3)-等变的“先编码后旋转”方案，从而从共享场景编码中生成抓取对齐的几何特征。对于抓取平移，中间动作融合（MAF）在全局聚合之前将抓取位置注入中间特征，保留每个候选相对的局部几何。我们在两种抓取规划流程中评估EquiGQNet：基于交叉熵方法（CEM）的连续抓取搜索和预训练生成规划器的候选排序。在仿真中，EquiGQNet实现了与早期融合基线相当的抓取性能，并在具有复杂几何和有限可抓取区域的物体上显著优于晚期融合，同时将CEM规划时间从3.31秒减少到0.48秒，相比早期融合加速6.9倍。在真实世界的家庭物体整理中，EquiGQNet实现了95.2%的抓取成功率和每小时230次拾取，而早期和晚期融合基线分别为153次和170次。代码可在该https URL获取。
英文摘要
Planning six-degree-of-freedom (6-DoF) grasps for unseen objects in cluttered tabletop scenes from a single-view depth image requires accurate and efficient evaluation of diverse grasp candidates. Existing early-fusion methods capture local object geometry relative to each grasp candidate but repeatedly encode the scene, whereas late-fusion methods reuse a shared scene representation but may lose this grasp-relative local geometry. We propose EquiGQNet, an efficient 6-DoF grasp quality evaluator that combines the strengths of both approaches. For grasp orientation, EquiGQNet replaces the early-fusion operation of rotating and re-encoding the point cloud for each grasp candidate with an SO(3)-equivariant encode-once-then-rotate scheme, yielding grasp-aligned geometric features from a shared scene encoding. For grasp translation, Mid-level Action Fusion (MAF) injects the grasp position into intermediate features before global aggregation, retaining local geometry relative to each candidate. We evaluate EquiGQNet in two grasp planning pipelines: Cross-Entropy Method (CEM)-based continuous grasp search and candidate ranking with a pretrained generative planner. In simulation, EquiGQNet achieves grasping performance comparable to the early-fusion baseline and substantially outperforms late fusion on objects with complex geometry and limited graspable regions, while reducing CEM planning time from 3.31s to 0.48s, a 6.9x speedup over early fusion. In real-world household-object decluttering, EquiGQNet achieves a 95.2% grasp success rate and 230 picks per hour, versus 153 and 170 for early- and late-fusion baselines. Code is available at https://equigqnet.github.io/.
Comments8 pages, 6 figures, 4 tables. Submitted to IEEE Robotics and Automation Letters (RA-L)
