人机物理交互中机器人行为偏好的预测能力与温暖度
Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction
摘要：
在人机交互（HRI）中理解人类感知和偏好的可靠方法在设计现实世界的HRI中至关重要。社会认知认为，温暖和能力这两个维度是表征其他人的核心和普遍维度。机器人社会属性量表（RoSAS）提出了适合HRI的这些维度的项目，并在视觉观察研究中对其进行了验证。在本文中，我们补充验证，显示这些尺寸的可用性，在一个基于行为的，物理HRI研究与一个完全自主的机器人。我们将研究结果与流行的“一路顺风”维度进行了比较，这些维度包括：生命力、拟人性、亲和力、感知智力和感知安全。我们发现，在所有RoSAS和Godspeed维度中，Warmth和Competence是人类对不同机器人行为偏好的最重要预测因素。即使在条件之间没有明确的共识偏好或显著的因素差异时，这种预测能力也是有效的。
英文摘要：
A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
