DecoVLN：解耦观测、推理与纠正的视觉语言导航
DecoVLN: Decoupling Observation, Reasoning, and Correction for Vision-and-Language Navigation
长时记忆构建被形式化为对统一评分函数的迭代优化（语义相关性、视觉多样性、时序覆盖），可直接迁移到任何需要从历史帧池中挑选关键帧的视觉导航或视频理解流水线；状态-动作对级纠错微调利用测地距离筛选可信区域样本，为视觉导航中的复合误差问题提供了可复用的数据筛选思路。
原文依据 we formulate long-term memory construction as an optimization problem and introduce adaptive refinement mechanism
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论视觉语言导航（VLN）要求智能体遵循长时程指令并在复杂三维环境中导航。然而，现有方法面临两大挑战：构建有效的长期记忆库以及克服复合误差问题。为解决这些问题，我们提出 DecoVLN，一个面向长时程导航中鲁棒流式感知与闭环控制的有效框架。首先，我们将长期记忆构建形式化为一个优化问题，并引入自适应精炼机制，通过迭代优化一个统一评分函数从历史候选池中选取帧。该函数联合平衡三项关键准则：与指令的语义相关性、所选记忆的视觉多样性以及历史轨迹的时序覆盖度。其次，为缓解复合误差，我们引入状态-动作对级别的纠正性微调策略。通过利用状态之间的测地距离精确量化与专家轨迹的偏差，智能体在可信区域内收集高质量状态-动作对，同时过滤掉相关性低的受污染数据。这提升了误差纠正的效率与稳定性。大量实验证明了 DecoVLN 的有效性，并且我们已将其部署于真实环境中。
英文原文摘要 1386 字符
Vision-and-Language Navigation (VLN) requires agents to follow long-horizon instructions and navigate complex 3D environments. However, existing approaches face two major challenges: constructing an effective long-term memory bank and overcoming the compounding errors problem. To address these issues, we propose DecoVLN, an effective framework designed for robust streaming perception and closed-loop control in long-horizon navigation. First, we formulate long-term memory construction as an optimization problem and introduce adaptive refinement mechanism that selects frames from a historical candidate pool by iteratively optimizing a unified scoring function. This function jointly balances three key criteria: semantic relevance to the instruction, visual diversity from the selected memory, and temporal coverage of the historical trajectory. Second, to alleviate compounding errors, we introduce a state-action pair-level corrective finetuning strategy. By leveraging geodesic distance between states to precisely quantify deviation from the expert trajectory, the agent collects high-quality state-action pairs in the trusted region while filtering out the polluted data with low relevance. This improves both the efficiency and stability of error correction. Extensive experiments demonstrate the effectiveness of DecoVLN, and we have deployed it in real-world environments.