机器人抓取空间中的连通性感知探索
Connectivity-Aware Exploration of Robotic Grasp Spaces
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中文总结 AI 辅助
本文研究机器人抓取空间中成功动作的连通性结构，提出连通性感知采样策略，通过优先探索结构桥梁与边界，显著提升抓取空间重建效率，并验证结构经验可迁移至新物体。
中文摘要 AI 辅助
机器人抓取通常被表述为从候选抓取姿态空间中识别成功动作的问题。然而，成功动作在该空间内的组织方式受到的关注较少。我们研究了$SE(3)$中可行机器人抓取的多尺度结构，并探讨该结构是否可被利用以实现更高效的探索。利用大规模抓取数据集，我们表明成功抓取集在不同物体间展现出异质且可复现的连通性结构。随后，我们提出一种连通性感知采样策略，该策略通过优先考虑组件间的潜在桥梁、结构前沿、边界扩展和几何新颖性，逐步探索当前观测到的抓取空间。在受控重建实验中，该方法恢复成功抓取集连通性结构的效率显著高于随机采样和最远点采样。我们进一步评估了在隐藏抓取可行性条件下获取的连通性是否能改善后续抓取发现，以及先前探索物体的结构经验能否被检索并迁移到未见物体上。这些结果表明，可行动作的空间组织提供了超越单个候选动作可行性本身、与抓取空间探索相关的信息。更广泛地说，它们激励了结构感知探索作为利用机器人操作中可行动作空间几何结构的一种手段。
英文摘要
Robotic grasping is typically formulated as the problem of identifying successful actions from a space of candidate grasp poses. However, the organization of successful actions within this space has received less attention. We study the multiscale structure of viable robotic grasps in $SE(3)$ and investigate whether this structure can be exploited for more efficient exploration. Using a large-scale grasp dataset, we show that successful grasp sets exhibit heterogeneous and reproducible connectivity structure across objects. We then introduce a connectivity-aware sampling strategy that incrementally explores the currently observed grasp space by prioritizing potential bridges between components, structural frontiers, boundary extensions, and geometric novelty. In controlled reconstruction experiments, the method recovers the connectivity structure of successful grasp sets substantially more efficiently than random sampling and farthest-point sampling. We further evaluate whether connectivity acquired under hidden grasp viability can improve subsequent grasp discovery, and whether structural experience from previously explored objects can be retrieved and transferred to unseen objects. These results suggest that the spatial organization of viable actions provides information relevant to grasp-space exploration beyond the viability of individual candidate actions. More broadly, they motivate structure-aware exploration as a means of exploiting the geometry of viable action spaces in robotic manipulation.
