Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909950583767040 |
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| author | Wei, Meng Wan, Chenyang Peng, Jiaqi Yu, Xiqian Yang, Yuqiang Feng, Delin Cai, Wenzhe Zhu, Chenming Wang, Tai Pang, Jiangmiao Liu, Xihui |
| author_facet | Wei, Meng Wan, Chenyang Peng, Jiaqi Yu, Xiqian Yang, Yuqiang Feng, Delin Cai, Wenzhe Zhu, Chenming Wang, Tai Pang, Jiangmiao Liu, Xihui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08186 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation Wei, Meng Wan, Chenyang Peng, Jiaqi Yu, Xiqian Yang, Yuqiang Feng, Delin Cai, Wenzhe Zhu, Chenming Wang, Tai Pang, Jiangmiao Liu, Xihui Robotics 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. |
| title | Ground Slow, Move Fast: A Dual-System Foundation Model for Generalizable Vision-and-Language Navigation |
| topic | Robotics |
| url | https://arxiv.org/abs/2512.08186 |