用大模型动态排序探索前沿，提升无地图导航效率
LGR: LLM-Guided Ranking of Frontiers for Object Goal Navigation
- 用大模型根据视觉上下文判断多个前沿的相对价值
- 在Habitat-Sim中比基线方法成功率提高12.3%
- 适合需要动态环境导航的机器人应用
目标导向导航（OGN）是机器人与人工智能的基础任务，广泛应用于移动机器人图像数据库（MRID）等场景。其中，无地图OGN在未知或动态环境中尤为重要。本文旨在通过利用大语言模型（LLM）的常识推理能力，改进现有模块化无地图OGN系统。针对前沿探索中访问顺序难以确定的问题，我们将该问题建模为前沿排序任务。基于最新发现：尽管LLM无法判断单个前沿的绝对价值，但能在单张视图中有效评估多个前沿的相对价值。我们动态维护前沿列表，以LLM作为排序模型，将排名结果表示为倒数秩向量，便于多视角、多查询信息融合。在Habitat-Sim上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Object Goal Navigation (OGN) is a fundamental task for robots and AI, with key applications such as mobile robot image databases (MRID). In particular, mapless OGN is essential in scenarios involving unknown or dynamic environments. This study aims to enhance recent modular mapless OGN systems by leveraging the commonsense reasoning capabilities of large language models (LLMs). Specifically, we address the challenge of determining the visiting order in frontier-based exploration by framing it as a frontier ranking problem. Our approach is grounded in recent findings that, while LLMs cannot determine the absolute value of a frontier, they excel at evaluating the relative value between multiple frontiers viewed within a single image using the view image as context. We dynamically manage the frontier list by adding and removing elements, using an LLM as a ranking model. The ranking results are represented as reciprocal rank vectors, which are ideal for multi-view, multi-query information fusion. We validate the effectiveness of our method through evaluations in Habitat-Sim.
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