{"ID":2845314,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.04320","arxiv_id":"2511.04320","title":"MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments","abstract":"Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity, and global structure to support high-level decision making under partial observability. Existing approaches struggle to efficiently capture such multi-scale spatial understanding while maintaining low computational cost for real-time navigation. We present MacroNav, a learning-based navigation framework featuring two key components: (1) a lightweight context encoder trained via multi-task self-supervised learning to capture multi-scale, navigation-centric spatial representations; and (2) a reinforcement learning policy that seamlessly integrates these representations with graph-based reasoning for efficient action selection. Extensive experiments demonstrate the context encoder's effective and robust environmental understanding. Real-world deployments further validate MacroNav's effectiveness, yielding significant gains over state-of-the-art navigation methods in both Success Rate (SR) and Success weighted by Path Length (SPL), with superior computational efficiency.","short_abstract":"Autonomous navigation in unknown environments requires multi-scale spatial understanding that captures geometric details, topological connectivity, and global structure to support high-level decision making under partial observability. Existing approaches struggle to efficiently capture such multi-scale spatial underst...","url_abs":"https://arxiv.org/abs/2511.04320","url_pdf":"https://arxiv.org/pdf/2511.04320v2","authors":"[\"Kuankuan Sima\",\"Longbin Tang\",\"Zhenyu Yang\",\"Haozhe Ma\",\"Lin Zhao\"]","published":"2025-11-06T12:47:33Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}