@inproceedings{zhang-etal-2025-citynavagent,
title = "{C}ity{N}av{A}gent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory",
author = "Zhang, Weichen and
Gao, Chen and
Yu, Shiquan and
Peng, Ruiying and
Zhao, Baining and
Zhang, Qian and
Cui, Jinqiang and
Chen, Xinlei and
Li, Yong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1511/",
doi = "10.18653/v1/2025.acl-long.1511",
pages = "31292--31309",
ISBN = "979-8-89176-251-0",
abstract = "Aerial vision-and-language navigation (VLN) {---} requiring drones to interpret natural language instructions and navigate complex urban environments {---} emerges as a critical embodied AI challenge that bridges human-robot interaction, 3D spatial reasoning, and real-world deployment. Although existing ground VLN agents achieved notable results in indoor and outdoor settings, they struggle in aerial VLN due to the absence of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. In this work, we propose \textbf{CityNavAgent}, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN. Specifically, we design a hierarchical semantic planning module (HSPM) that decomposes the long-horizon task into sub-goals with different semantic levels. The agent reaches the target progressively by achieving sub-goals with different capacities of the LLM. Additionally, a global memory module storing historical trajectories into a topological graph is developed to simplify navigation for visited targets. Extensive benchmark experiments show that our method achieves state-of-the-art performance with significant improvement. Further experiments demonstrate the effectiveness of different modules of CityNavAgent for aerial VLN in continuous city environments."
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<abstract>Aerial vision-and-language navigation (VLN) — requiring drones to interpret natural language instructions and navigate complex urban environments — emerges as a critical embodied AI challenge that bridges human-robot interaction, 3D spatial reasoning, and real-world deployment. Although existing ground VLN agents achieved notable results in indoor and outdoor settings, they struggle in aerial VLN due to the absence of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. In this work, we propose CityNavAgent, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN. Specifically, we design a hierarchical semantic planning module (HSPM) that decomposes the long-horizon task into sub-goals with different semantic levels. The agent reaches the target progressively by achieving sub-goals with different capacities of the LLM. Additionally, a global memory module storing historical trajectories into a topological graph is developed to simplify navigation for visited targets. Extensive benchmark experiments show that our method achieves state-of-the-art performance with significant improvement. Further experiments demonstrate the effectiveness of different modules of CityNavAgent for aerial VLN in continuous city environments.</abstract>
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%0 Conference Proceedings
%T CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory
%A Zhang, Weichen
%A Gao, Chen
%A Yu, Shiquan
%A Peng, Ruiying
%A Zhao, Baining
%A Zhang, Qian
%A Cui, Jinqiang
%A Chen, Xinlei
%A Li, Yong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zhang-etal-2025-citynavagent
%X Aerial vision-and-language navigation (VLN) — requiring drones to interpret natural language instructions and navigate complex urban environments — emerges as a critical embodied AI challenge that bridges human-robot interaction, 3D spatial reasoning, and real-world deployment. Although existing ground VLN agents achieved notable results in indoor and outdoor settings, they struggle in aerial VLN due to the absence of predefined navigation graphs and the exponentially expanding action space in long-horizon exploration. In this work, we propose CityNavAgent, a large language model (LLM)-empowered agent that significantly reduces the navigation complexity for urban aerial VLN. Specifically, we design a hierarchical semantic planning module (HSPM) that decomposes the long-horizon task into sub-goals with different semantic levels. The agent reaches the target progressively by achieving sub-goals with different capacities of the LLM. Additionally, a global memory module storing historical trajectories into a topological graph is developed to simplify navigation for visited targets. Extensive benchmark experiments show that our method achieves state-of-the-art performance with significant improvement. Further experiments demonstrate the effectiveness of different modules of CityNavAgent for aerial VLN in continuous city environments.
%R 10.18653/v1/2025.acl-long.1511
%U https://aclanthology.org/2025.acl-long.1511/
%U https://doi.org/10.18653/v1/2025.acl-long.1511
%P 31292-31309
Markdown (Informal)
[CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory](https://aclanthology.org/2025.acl-long.1511/) (Zhang et al., ACL 2025)
ACL
- Weichen Zhang, Chen Gao, Shiquan Yu, Ruiying Peng, Baining Zhao, Qian Zhang, Jinqiang Cui, Xinlei Chen, and Yong Li. 2025. CityNavAgent: Aerial Vision-and-Language Navigation with Hierarchical Semantic Planning and Global Memory. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 31292–31309, Vienna, Austria. Association for Computational Linguistics.