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arXiv 2606.28397cs.CVcs.AI

CLOSER-VLN:面向空中视觉语言导航的闭环自验证检索增强推理

CLOSER-VLN: Closed-Loop Self-Verified Retrieval-Augmented Reasoning for Aerial Vision-Language Navigation

Shaoxuan Li, Xiangyu Dong, Xiaoguang Ma, Junfeng Chen, Haoran Zhao, Yaoming Zhou

AI总结:

提出CLOSER方法,通过闭环的推理、验证、检索和纠正机制,解决空中VLN中开放循环决策导致的误差累积问题,在CityNav基准上取得32.01% SR和21.28% SPL。

摘要:

视觉语言导航(VLN)最近随着大型语言和多模态模型的发展而进步,使智能体能够在不训练特定任务导航策略的情况下,根据自然语言指令在未知环境中导航。然而,大多数依赖大型模型的现有VLN方法仍然采用开环决策-执行方法,其中候选动作从指令和观察中生成,但在执行前很少被验证或纠正。这在空中VLN中导致关键问题,即中间动作的微小误差可能迅速累积成大的轨迹偏差并导致目标丢失。为了解决这个问题,我们提出了闭环自验证检索增强推理(CLOSER),一种无需训练策略的方法,在执行具体动作前以闭环方式顺序执行动作推理、可靠性验证、目标检索和动作纠正。我们在空中VLN任务中实例化CLOSER,并开发了CLOSER-VLN框架,该框架由三个组件组成:基于可用信息生成候选动作的层次推理器、评估推理器生成动作可靠性的多维动作验证器,以及仅在验证失败时从记忆库中检索目标示例的验证触发多模态检索器。我们在CityNav基准上进行实验评估,CLOSER-VLN在测试未见分割上实现了32.01%的SR和21.28%的SPL,证实了闭环推理的有效性。

英文摘要:

Vision-language navigation (VLN) has recently advanced with large language and multimodal models, enabling agents to follow natural-language instructions in unseen environments without training a task-specific navigation policy. However, most existing VLN methods relying on large models still adopt an open-loop decision-execution approach, where candidate actions are generated from instructions and observations but are rarely verified or corrected before execution. This causes critical issues in aerial VLN, where minor errors in intermediate actions may quickly accumulate into large trajectory deviations and lead to target loss. To address this issue, we propose Closed-loop Self-verified Retrieval-augmented Reasoning (CLOSER), a training-policy-free method that sequentially performs action reasoning, reliability verification, targeted retrieval, and action correction in a closed-loop manner before executing concrete actions. We instantiate the CLOSER in aerial VLN tasks and develop a CLOSER-VLN framework, which is composed of three components: a hierarchical reasoner for generating candidate actions based on available information, a multidimensional action verifier for assessing the reliability of actions generated by the reasoner, and a verification-triggered multimodal retriever for retrieving targeted exemplars from a memory bank only when verification fails. We conduct experimental evaluations on the CityNav benchmark, where CLOSER-VLN achieves 32.01% SR and 21.28% SPL on the test-unseen split, confirming the effectiveness of closed-loop reasoning.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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