MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918458239746048 |
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| author | Sima, Kuankuan Tang, Longbin Yang, Zhenyu Ma, Haozhe Zhao, Lin |
| author_facet | Sima, Kuankuan Tang, Longbin Yang, Zhenyu Ma, Haozhe Zhao, Lin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_04320 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments Sima, Kuankuan Tang, Longbin Yang, Zhenyu Ma, Haozhe Zhao, Lin Robotics 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. |
| title | MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.04320 |