发表机构
University of Waterloo; Ontario Tech University; York University; University of Toronto（滑铁卢大学; 安大略理工大学; 约克大学; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人员目标导航问题，提出结合距离与语义信息并按信任度加权的规划框架，并开发合成基准场景；实验表明优于距离基线，支持真实世界具身实现。
AI 中文摘要
我们针对人员目标导航（PersonNav）问题，使机器人能够在信息可访问性和语言不确定性的现实约束下搜索人员。这解决了当前解决方案围绕刚性人员查找系统的问题，这些系统需要精确了解个体以在室内环境中进行物品递送。文献中的重点在于使用因人类隐私而不可用的公共或稀疏数据进行学习的方法。我们提出了一种规划框架，结合了关于接收者（习惯和意图）的距离和语义信息，并根据用户提供信息的信任度进行加权。我们还开发了一个合成基准场景，包括行动者、物品和请求，以在实际部署前评估性能，旨在模拟自然语言的人机交互。结果表明，我们的信息化搜索优于经典的基于距离的图基线，而仅凭语义会导致无根据的、零星的搜索。我们的方法在基线上取得了显著提升，其中基于LLM的变体表现相当，其显式信念表示自然支持未来的贝叶斯滤波。硬件测试表明，我们的方法支持真实世界的具身实现，能够在实际环境中利用语义和距离信息。这项工作朝着更智能的移动服务代理迈进，能够在现实环境中进行类似人类的、信息化的搜索，以供日常使用。此 https URL。
英文摘要
We address the Person Goal Navigation (PersonNav) problem, enabling robots to search for people under realistic constraints on information accessibility and language uncertainty. This tackles the current solutions revolving around rigid person finding systems requiring exact knowledge of the individual for item-delivery in indoor settings. While the focus in literature is on learned methods using unavailable public or sparse data due to human privacy. We propose a planning framework that combines distance and semantic information about the recipient (habits and intent) weighted by the trust of the user-provided information. A synthetic benchmark of scenarios is also developed including actors, items, and requests to evaluate performance before real-world deployment aiming to simulate natural language human-robot interactions. Results show our informed search outperforms classical distance-based graph baselines, while semantics alone lead to ungrounded, sporadic search. Our method achieves strong gains over baselines, with an LLM-based variant performing comparably, and its explicit belief representation naturally supports future Bayesian filtering. Hardware tests demonstrate our method supports real-world embodiment able to leverage between semantic and distance information in a real setting. This work moves toward more intelligent mobile service agents capable of human-like, informed search in realistic environments to be leveraged in day-to-day use. https://anonymous.4open.science/r/personnavsite-4ED2/index.html.
