MacroNav: マルチタスク文脈表現学習による未知環境での効率的ナビゲーション
MacroNav: Multi-Task Context Representation Learning Enables Efficient Navigation in Unknown Environments
マルチタスク自己教師あり学習で多スケールの空間表現を獲得し、強化学習とグラフ推論を組み合わせて未知環境を効率的にナビゲートする手法を提案した。
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著者: Kuankuan Sima, Longbin Tang, Zhenyu Yang, Haozhe Ma, Lin Zhao
分類: cs.RO
原文アブストラクト
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.
