Global Commander and Local Operative: A Dual-Agent Framework for Scene Navigation

• URL: http://arxiv.org/abs/2602.18941v1
• Date: Sat, 21 Feb 2026 19:19:55 GMT
• Title: Global Commander and Local Operative: A Dual-Agent Framework for Scene Navigation
• Authors: Kaiming Jin, Yuefan Wu, Shengqiong Wu, Bobo Li, Shuicheng Yan, Tat-Seng Chua,
• Abstract summary: Vision-and-Language Scene navigation is a fundamental capability for embodied human-AI.<n>We introduce DACo, a planning-grounding decoupled architecture that disentangles global deliberation from local grounding.<n>By disentangling global reasoning from local action, DACo alleviates cognitive overload and improves long-horizon stability.
• Score: 96.88162755522342
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Vision-and-Language Scene navigation is a fundamental capability for embodied human-AI collaboration, requiring agents to follow natural language instructions to execute coherent action sequences in complex environments. Existing approaches either rely on multiple agents, incurring high coordination and resource costs, or adopt a single-agent paradigm, which overloads the agent with both global planning and local perception, often leading to degraded reasoning and instruction drift in long-horizon settings. To address these issues, we introduce DACo, a planning-grounding decoupled architecture that disentangles global deliberation from local grounding. Concretely, it employs a Global Commander for high-level strategic planning and a Local Operative for egocentric observing and fine-grained execution. By disentangling global reasoning from local action, DACo alleviates cognitive overload and improves long-horizon stability. The framework further integrates dynamic subgoal planning and adaptive replanning to enable structured and resilient navigation. Extensive evaluations on R2R, REVERIE, and R4R demonstrate that DACo achieves 4.9%, 6.5%, 5.4% absolute improvements over the best-performing baselines in zero-shot settings, and generalizes effectively across both closed-source (e.g., GPT-4o) and open-source (e.g., Qwen-VL Series) backbones. DACo provides a principled and extensible paradigm for robust long-horizon navigation. Project page: https://github.com/ChocoWu/DACo

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