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arXiv Machine Learning By Maksim A Kazanskii

Connectivity-Aware Exploration of Robotic Grasp Spaces

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The paper investigates the multiscale connectivity structure of successful robotic grasps in SE(3) and finds that these sets exhibit heterogeneous yet reproducible connectivity across objects. It proposes a connectivity‑aware sampling strategy that prioritizes bridges, frontiers, boundary extensions, and geometric novelty, which recovers grasp connectivity more efficiently than random or farthest‑point sampling. Experiments also show that connectivity information can improve subsequent grasp discovery and transfer to unseen objects, indicating that the spatial organization of viable actions offers valuable guidance for exploration.

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