HTNav: 都市空中視覚言語ナビゲーションのための階層構造を持つハイブリッドナビゲーションフレームワーク
HTNav: A Hybrid Navigation Framework with Tiered Structure for Urban Aerial Vision-and-Language Navigation
都市環境での空中VLNタスクに対し、模倣学習と強化学習を組み合わせたハイブリッドフレームワークを提案し、段階的訓練と階層的意思決定によりナビゲーション精度とロバスト性を向上させた。
詳しい要約
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著者: Chengjie Fan, Cong Pan, Zijian Liu, Ningzhong Liu, Jie Qin
分類: cs.RO, cs.AI
原文アブストラクト
Inspired by the general Vision-and-Language Navigation (VLN) task, aerial VLN has attracted widespread attention, owing to its significant practical value in applications such as logistics delivery and urban inspection. However, existing methods face several challenges in complex urban environments, including insufficient generalization to unseen scenes, suboptimal performance in long-range path planning, and inadequate understanding of spatial continuity. To address these challenges, we propose HTNav, a new collaborative navigation framework that integrates Imitation Learning (IL) and Reinforcement Learning (RL) within a hybrid IL-RL framework. This framework adopts a staged training mechanism to ensure the stability of the basic navigation strategy while enhancing its environmental exploration capability. By integrating a tiered decision-making mechanism, it achieves collaborative interaction between macro-level path planning and fine-grained action control. Furthermore, a map representation learning module is introduced to deepen its understanding of spatial continuity in open domains. On the CityNav benchmark, our method achieves state-of-the-art performance across all scene levels and task difficulties. Experimental results demonstrate that this framework significantly improves navigation precision and robustness in complex urban environments.
