用多任务学习构建高效导航的多尺度空间表征
MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
- 通过自监督多任务学习训练轻量级上下文编码器，捕获多尺度空间特征
- 在真实环境测试中，成功率和路径加权成功率均显著优于现有方法
- 适合需要低延迟、高鲁棒性的机器人自主导航场景
未知环境中自主导航需要多尺度的空间理解能力，以捕捉几何细节、拓扑连接和全局结构，支持部分可观测条件下的高层决策。现有方法难以在保持低计算成本的同时高效获取此类多尺度空间理解。我们提出MacroNav，一个基于学习的导航框架，包含两个关键组件：(1) 通过多任务自监督学习训练的轻量级上下文编码器，用于捕获多尺度、导航中心的空间表征；(2) 与图推理无缝集成的强化学习策略，实现高效的动作选择。大量实验表明，该编码器能有效且稳健地理解环境。真实世界部署进一步验证了MacroNav的有效性，在成功率（SR）和路径长度加权成功率（SPL）上均显著优于当前最先进方法，且计算效率更高。
原文摘要 · Abstract (English)
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.
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