发表机构
Cornell University; SiLC Technologies; Draper（康奈尔大学; SiLC Technologies; 德雷珀）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于社交力的导游机器人导航模型，通过考虑障碍物、用户位置和朝向等因素，并设计实验评估其对可跟随性、感知安全性和感知智能的影响。
AI 中文摘要
我们提出了一种基于力的模型，用于导游机器人的社交导航。该模型考虑了由障碍物、用户位置和朝向等多种因素产生的社交力。我们声称，这些力中的每一个都使机器人对用户更具社交性，并为此设计了一个实验设置进行评估。在实验中，用户跟随一个自主机器人前往已知地图中的目的地，同时中途执行几个简单的子任务，这些子任务起到干扰作用。对于每位参与者，我们进行了多轮实验，每轮使用不同的力以及最短路径（A*搜索基线模型）。通过每轮的主观指标，我们提出研究我们的力模型对诸如可跟随性、感知安全性和感知智能等构念的影响。
英文摘要
We propose a force-based model for social navigation of a tour-guide robot. Social forces due to various factors like obstacles, user position and heading, have been accounted for in the model. We claim that each one of these forces makes the robot more sociable to the user and we design an experimental setup for evaluation. In the experiment, the user follows an autonomous robot to a destination in a known map, while undertaking a few simple sub-tasks in the middle, which serve as distractions. For each participant, we run several rounds of the experiment, each with different forces and a shortest path, A*-search baseline model. Using per-round subjective indicators, we propose to study the effect of our force model on constructs such as: follow-ability, perceived safety, and perceived intelligence.
Comments5 pages, 8 figures. Accepted at the 5th Workshop on Social Robot Navigation, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026
