Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration
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 tha...
| Authors | Adithya V. Sastry, Bibek Poudel, Weizi Li |
|---|---|
| Primary category | cs.RO |
| Published | 15 April 2026 |
| Last updated at source | 13 August 2026 |
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