SOURCE-LINKED INTELLIGENCE
Connectivity-Aware Exploration of Robotic Grasp Spaces
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 incremen
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- arXiv · AI, language, vision and robotics · 2026-09-19T12:34:42.000Z
First collected: 2026-09-23T10:01:48.231Z. This is not the publication date.