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.

本文介绍了利用社会认知中的温暖和能力作为两个基本维度，结合机器人社会属性量表（RoSAS）和Godspeed模型，实现对人和机器人交互过程中的感知和偏好进行研究及预测的方法。结果发现，RoSAS中的温暖和能力对人类在机器人交互中的行为偏好具有较强的预测力。

在人机交互中使用温暖和能力来预测人类对机器人行为的偏好