While recent large vision-language models (VLMs) have improved generalization in vision-language navigation (VLN), existing methods typically rely on end-to-end pipelines that map vision-language inputs directly to short-horizon discrete actions. Such designs often produce fragmented motions, incur high latency, and struggle with real-world challenges like dynamic obstacle avoidance. We propose DualVLN, the first dual-system VLN foundation model that synergistically integrates high-level reasoning with low-level action execution. System 2, a VLM-based global planner, "grounds slowly" by predicting mid-term waypoint goals via image-grounded reasoning. System 1, a lightweight, multi-modal conditioning Diffusion Transformer policy, "moves fast" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajectories. The dual-system design enables robust real-time control and adaptive local decision-making in complex, dynamic environments. By decoupling training, the VLM retains its generalization, while System 1 achieves interpretable and effective local navigation. DualVLN outperforms prior methods across all VLN benchmarks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability in dynamic environments.

翻译：尽管近期的大型视觉语言模型（VLMs）提升了视觉语言导航（VLN）的泛化能力，但现有方法通常依赖于端到端管道，直接将视觉语言输入映射为短视程的离散动作。此类设计常导致运动轨迹碎片化、延迟较高，且在应对动态避障等现实挑战时表现不佳。我们提出了DualVLN，首个双系统VLN基础模型，它协同整合了高层推理与低层动作执行。系统2是一个基于VLM的全局规划器，通过基于图像的推理预测中程航点目标，实现“稳扎稳打”。系统1是一个轻量级、多模态条件扩散Transformer策略，利用系统2提供的显式像素目标和潜在特征生成平滑精确的轨迹，实现“快速行动”。双系统设计支持在复杂动态环境中进行鲁棒的实时控制和自适应局部决策。通过解耦训练，VLM保持了其泛化能力，而系统1实现了可解释且高效的局部导航。DualVLN在所有VLN基准测试中均优于先前方法，真实世界实验验证了其在动态环境中具备鲁棒的长视程规划能力和实时适应性。