DA-Nav: Direction-Aware City-Scale Vision-Language Navigation

Jul 16, 2026

City-scale outdoor navigation is constrained by dependence on dense maps and costly navigation supervision. The work proposes DA-Nav, a direction-aware vision-language navigation framework that converts coarse directional instructions from commercial tools into discrete, executable spatial grounding on the egocentric 2D image plane. Robust trajectory recovery mitigates long-horizon error accumulation via chain-of-thought reasoning for deviation assessment, action prediction, and target grid selection. ReDA dataset provides direction-aware instructions and recovery trajectories. Experiments in CARLA show 56.16% success on unseen cities and improved recovery capability, with sim-to-real adaptation to quadruped and humanoid robots for stable kilometer-scale closed-loop navigation without fine-tuning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

Introduction

City-scale outdoor navigation challenges

Direction-aware vision-language navigation paradigm

DA-Nav approach and discrete spatial grounding

Trajectory recovery via chain-of-thought

ReDA dataset and experimental evaluation

DA-Nav: Direction-Aware City-Scale Vision-Language Navigation Ye Yuan∗, Kehan Chen∗ , Xinqiang Yu∗ , Wentao Xu, Heng Wang, Libo Huang, Chuanguang Yang, Yan Huang, Jiawei He†, Zhulin An† arXiv :2607 . 11638v1 [ cs .RO] 13 Jul 2026 Abstract—City-scale outdoor navigation is currently hindered by the heavy reliance on dense maps or costly navigation supervision. In this work, we introduce a novel paradigm for leveraging directional instructions from commercial navigation tools (e.g., Google Maps). To bridge the gap between commercial instructions and executable navigation actions, while mitigating long-horizon error accumulation through robust trajectory recovery, we propose DA-Nav, a Direction-Aware vision-language Navigation framework that reformulates navigation as a discrete spatial grounding problem on the egocentric 2D image plane. To achieve trajectory recovery, DA-Nav employs a Chainof-Thought (CoT) reasoning process encompassing deviation assessment, action prediction, and target grid selection. We further introduce ReDA, a dataset that provides directionaware instructions and recovery trajectories to enhance spatial grounding and support CoT recovery reasoning. Extensive experiments in CARLA demonstrate that DA-Nav achievesa high success rate of 56.16% in unseen urban environments, outperforming existing State-of-The-Art (SoTA) methods while maintaining a substantially stronger recovery capability. Furthermore, without fine-tuning, DA-Nav seamlessly adapts to both quadruped and humanoid robots, enabling stable kilometer-scale closed-loop outdoor navigation in complex realworld environments. Index Terms—Vision-Based Navigation, Data Sets for Robot Learning, Spatial Grounding, Trajectory Recovery, Sim-to-Real Transfer I. INTRODUCTION City-scale outdoor mobile robots hold significant potential for applications such as logistics delivery, automated inspection, and public services. However, reliable long-horizon urban navigation remains challenging. Existing approaches typically rely on Simultaneous Localization and Mapping (SLAM) and global path planning [1], [2] . In city-scale ∗ Ye Yuan, Kehan Chen, and Xinqiang Yu contributed equally to this work. †Zhulin An ([anzhulin@ict.ac.cn](anzhulin@ict.ac.cn)) and Jiawei He ( [jwhe2024@gmail.com](jwhe2024@gmail.com)) are the corresponding authors. Ye Yuan and Wentao Xu are with the School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China, and also with the Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.(email:{yuanye2024, [xuwt2024](xuwt2024}@shanghaitech.edu.cn)[}](xuwt2024}@shanghaitech.edu.cn)[@shanghaitech.edu.cn](xuwt2024}@shanghaitech.edu.cn)). Libo Huang, Chuanguang Yang, and Zhulin An are with the State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.(email:{yangchuanguang, [anzhulin](anzhulin}@ict.ac.cn)[}](anzhulin}@ict.ac.cn)[@ict.ac.cn](anzhulin}@ict.ac.cn) , [www.huanglibo@gmail.com](www.huanglibo@gmail.com)). Kehan Chen, Xinqiang Yu, and Yan Huang are with the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing, China. Kehan Chen and Xinqiang Yu are also with the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.(email:{xinqiang.yu, [yhuang](yhuang}@nlpr.ia.ac.cn)[}](yhuang}@nlpr.ia.ac.cn)[@nlpr.ia.ac.cn](yhuang}@nlpr.ia.ac.cn) , [kehan.chen@cripac.ia.ac.cn](kehan.chen@cripac.ia.ac.cn)). Ye Yuan, Heng Wang and Jiawei He are with XYZ Embodied AI, Beijing, China. scenarios, however, such methods often incur substantial mapping and maintenance costs and remain sensitive to dynamic environmental changes. To reduce the reliance on dense maps, recent studies have explored navigation using coarse-grained guidance signals such as natural language instructions, GPS waypoints, and semantic landmarks [4]–[9] .

FAQs

DA-Nav如何利用商用导航指令来实现城市尺度的机器人导航？

DA-Nav将来自商用导航工具的方向性指令转换为在自视2D图像平面上的离散空间落地问题，从而生成可执行的局部导航目标。该方法解决了人类可读指令难以直接驱动机器执行的问题。

长时导航中误差累积会带来什么问题，DA-Nav如何应对？

长时导航会因感知与控制误差逐渐偏离预期路径。DA-Nav通过链式推理过程进行偏差评估、动作预测以及目标网格选择，实现鲁棒的轨迹恢复。

ReDA数据集在DA-Nav训练与推理中扮演了什么角色？

ReDA提供方向感知的指令以及用于恢复轨迹的信息，帮助模型进行空间落地与链式推理式的恢复推理。该数据增强了模型的偏差恢复能力。

Jul 16, 2026

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