mlsanityData to 7 Oct 2026

SPARC: Spatial-Aware Path Planning via Attentive Agent Communication

arXiv 2603.02845 ↗First posted 3 Mar 2026cs.RO cs.AIcited by 0

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Abstract (arXiv)

Efficient communication is critical for decentralized Multi-Robot Path Planning (MRPP), yet existing learned communication methods treat all neighboring robots equally regardless of their spatial proximity, leading to diluted attention in congested regions where coordination matters most. We propose Relation enhanced Multi Head Attention (RMHA), a communication mechanism that explicitly embeds pairwise Manhattan distances into the attention weight computation, enabling each robot to dynamically prioritize messages from spatially relevant neighbors. Combined with a distance-constrained attention mask and GRU gated message fusion, RMHA integrates seamlessly with MAPPO for stable end-to-end training. In zero-shot generalization from 8 training robots to 128 test robots on 40x40 grids, RMHA achieves approximately 75 percent success rate at 30 percent obstacle density outperforming the best baseline by over 25 percentage points. Ablation studies confirm that distance-relation encoding is the key contributor to success rate improvement in high-density environments. Index Terms-Multi-robot path planning, graph attention mechanism, multi-head attention, communication optimization, cooperative decision-making

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References · 10

  1. 2019Graph Neural Networks for Decentralized Multi-Robot Path Planningcited by 84
  2. 2020Lifelong Multi-Agent Path Finding in Large-Scale Warehousescited by 79
  3. 2020Message-Aware Graph Attention Networks for Large-Scale Multi-Robot Path Planningcited by 41
  4. 2023SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfindingcited by 35
  5. 2021Distributed Heuristic Multi-Agent Path Finding with Communicationcited by 31
  6. 2023Multi-Robot Coordination and Layout Design for Automated Warehousingcited by 17
  7. 2021Subdimensional Expansion for Multi-objective Multi-agent Path Findingcited by 9
  8. 2022Graph-Based Multi-Robot Path Finding and Planningcited by 7
  9. 2024LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Findingcited by 7
  10. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5

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