将零样本物体导航建模为加权旅行维修工问题，提升复杂环境下的寻物效率。
Handle Object Navigation as Weighted Traveling Repairman Problem
- 把寻物任务看作最小化视角等待时间的加权旅行维修工问题
- 在复杂环境中实现比现有方法更高的导航准确率，支持动态目标更新
- 适合需要高效全局规划的机器人导航与开放词汇场景应用
零样本物体导航（ZSON）要求智能体在无预定义类别和先验环境知识的情况下，通过自然语言指令导航至指定物体。尽管近期方法利用基础模型或多模态地图，但仍常依赖二维表示、贪婪策略，或需额外训练与高计算开销模块，限制了其在复杂环境中的表现与实际应用。本文提出WTRP-Searcher框架，将ZSON建模为加权旅行维修工问题（WTRP），以最小化视角的加权等待时间。通过视觉语言模型（VLM）评估视角与目标描述的相似性，并结合深度信息投影到二维地图；开放词汇检测器识别目标并动态更新导航目标；3D嵌入特征图增强空间感知与环境记忆。实验表明，WTRP-Searcher在复杂任务中优于现有方法，实现高效全局规划与更优性能。代码与设计将在论文接受后开源。
原文摘要 · Abstract (English)
Zero-Shot Object Navigation (ZSON) requires agents to navigate to objects specified via open-ended natural language without predefined categories or prior environmental knowledge. While recent methods leverage foundation models or multi-modal maps, they often rely on 2D representations and greedy strategies or require additional training or modules with high computation load, limiting performance in complex environments and real applications. We propose WTRP-Searcher, a novel framework that formulates ZSON as a Weighted Traveling Repairman Problem (WTRP), minimizing the weighted waiting time of viewpoints. Using a Vision-Language Model (VLM), we score viewpoints based on object-description similarity, projected onto a 2D map with depth information. An open-vocabulary detector identifies targets, dynamically updating goals, while a 3D embedding feature map enhances spatial awareness and environmental recall. WTRP-Searcher outperforms existing methods, offering efficient global planning and improved performance in complex ZSON tasks. Code and design will be open-sourced upon acceptance.
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