Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration

Abstract:Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning. However, these graphs grow rapidly, accumulating redundant information and impacting performance. We present a hybrid transformer-based framework trained with Proximal Policy Optimization which complements exploration algorithms by pruning these graphs during exploration, limiting their growth and reducing the accumulation of excess information. The framework was evaluated on simulations of a robotic agent using Rapidly-Exploring Random Trees to carry out frontier-based exploration, where the learned policy reduces graph size by up to 96%. We find preliminary evidence that our framework enables effective, generalizable exploration under reduced information density, consistently outperforming the randomly pruned baseline and improving both exploration efficiency and computational efficiency beyond the unpruned baseline for complex long-horizon exploration tasks. To the best of our knowledge, these results are the first suggesting the viability of RL to prune dynamic graphs used in robotic exploration algorithms.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2604.16509 [cs.RO]

(or arXiv:2604.16509v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.16509 Focus to learn more arXiv-issued DOI via DataCite

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