HTNav：面向城市空中视觉语言导航的混合分层结构导航框架
HTNav: A Hybrid Navigation Framework with Tiered Structure for Urban Aerial Vision-and-Language Navigation
提出混合模仿学习与强化学习的分层导航框架，结合地图表征学习增强空间连续性理解，可复用于视觉语言导航等具身任务中的长程路径规划与精细动作控制协同问题。
原文依据 we propose HTNav, a new collaborative navigation framework that integrates Imitation Learning (IL) and Reinforcement Learning (RL) within a hybrid IL-RL framework.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论受通用视觉语言导航（VLN）任务启发，空中VLN因其在物流配送和城市巡检等应用中的重要实用价值而受到广泛关注。然而，现有方法在复杂城市环境中面临若干挑战，包括对未见场景的泛化不足、长程路径规划性能欠佳以及对空间连续性的理解不充分。为解决这些挑战，我们提出HTNav，一种新的协同导航框架，在混合模仿学习-强化学习（IL-RL）框架内整合模仿学习（IL）与强化学习（RL）。该框架采用分阶段训练机制，在确保基础导航策略稳定性的同时增强其环境探索能力。通过集成分层决策机制，实现宏观路径规划与细粒度动作控制之间的协同交互。此外，引入地图表征学习模块以加深对开放域空间连续性的理解。在CityNav基准上，我们的方法在所有场景级别和任务难度上均达到最优性能。实验结果表明，该框架显著提升了复杂城市环境中的导航精度与鲁棒性。
英文原文摘要 1370 字符
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.