基于环境条件和个人因素的舒适接近距离初步模型

Brittany A. Duncan, R. Murphy
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引用次数: 8

摘要

本文提出了一个“舒适距离”模型,该模型捕捉了已知的影响人类和人机交互中个人空间的因素和条件,以及它们之间任何已确定的关系。在第一次关于接近距离的人机交互(HRI)调查中,回顾了19篇论文,并综合了三种不同类型的输入:环境条件、个人因素和代理因素的“舒适距离”模型。五个环境条件(照明、天花板高度、室内/室外、房间大小、屏障高度)和七个个人因素(性别、年龄、情绪、性格、宠物是否拥有、机器人经验、坐/站)构成了模型,四个agent因素(接近角度、agent高度、接近速度、凝视)作为调节参数,产生适当距离的行为。目前,HRI研究者一般只关注一个因素(如趋近角度或趋近速度),而没有考虑邻近领域的前期工作,如心理学和其他社会科学。这导致环境因素被HRI社区所忽视。“舒适距离”模型是人力资源研究人员的一种新工具,它是可扩展的,因此可以在确定新因素时纳入新因素。这项调查将告知研究人员在HRI领域以前被忽视的因素,并将允许未来的研究人员考虑已确定变量的影响,以创建更完整的实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A preliminary model for comfortable approach distance based on environmental conditions and personal factors
This paper presents a model of “comfortable distance” that captures the factors and conditions known to affect personal space in human-human and human-robot interactions, as well as any identified relationships between them. In the first known human-robot interaction (HRI) survey on approach distance, 19 papers were reviewed and the “comfortable distance” model was synthesized with three distinct types of inputs: environmental conditions, personal factors, and agent factors. Five environmental conditions (lighting, ceiling height, indoor/outdoor, room size, and barrier height) and seven personal factors (gender, age, mood, personality, pet ownership, robot experience, and sitting/standing) structure the model, with four agent factors (angle of approach, height of agent, speed of approach, and gaze) used as tuning parameters to produce behaviors with appropriate distances. Currently, HRI researchers generally focus on one factor at a time (e.g., approach angle or approach speed), without considering the previous work in adjacent fields, such as psychology and other social sciences. This has resulted in environmental factors being ignored by the HRI community. The “comfortable distance” model is a new tool for HRI researchers and is expandable so that it can incorporate new factors as they are identified. This survey will inform researchers about factors which had been previously overlooked in the field of HRI and will allow future researchers to consider the impact of identified variables to create more complete experiments.
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