mlsanityData to 14 Oct 2026

Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration

arXiv 2604.16509 ↗First posted 15 Apr 2026cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 15 Apr 2026.

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References · 16

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2017Proximal Policy Optimization Algorithmscited by 15,215
  5. 2017Graph Attention Networkscited by 8,923
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  7. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
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  9. 2019Stabilizing Transformers for Reinforcement Learningcited by 270
  10. 2017graph2vec: Learning Distributed Representations of Graphscited by 230
  11. 2023When Do Transformers Shine in RL? Decoupling Memory from Credit Assignmentcited by 63
  12. 2020Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphscited by 30
4 more
  1. 2023In-Context Reinforcement Learning for Variable Action Spacescited by 24
  2. 2021Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertaintycited by 7
  3. 2022Fast and Compute-efficient Sampling-based Local Exploration Planning via Distribution Learningcited by 6
  4. 2023Graph-based Simultaneous Coverage and Exploration Planning for Fast Multi-robot Searchcited by 2

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