Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation

• URL: http://arxiv.org/abs/2512.08186v1
• Date: Tue, 09 Dec 2025 02:29:36 GMT
• Title: Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation
• Abstract summary: We propose DualVLN, a vision-language navigation system that integrates high-level reasoning with low-level action execution.<n>System 1 "moves fast" by leveraging both explicit pixel goals and latent features from System 2 to generate smooth and accurate trajectories.<n>System outperforms prior methods across all VLN benchmarks and real-world experiments demonstrate robust long-horizon planning and real-time adaptability.
• Score: 45.54638103934175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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