论文标题
温暖和能力预测人类机器人互动中机器人行为的偏好
Warmth and Competence to Predict Human Preference of Robot Behavior in Physical Human-Robot Interaction
论文作者
论文摘要
了解人类互动（HRI）中人类感知和偏好的扎实方法对于设计现实世界的HRI至关重要。社会认知认为，尺寸的温暖和能力是描述其他人类的中心和普遍维度。机器人社会属性量表（ROSA）为适合HRI的那些维度提出了项目，并在视觉观察研究中验证了它们。在本文中，我们通过在具有完全自主的机器人的基于行为的物理HRI研究中显示这些维度的可用性来补充验证。我们将发现与流行的Godspeed Dimensions动画，拟人化，可爱性，感知的智力和感知的安全性进行了比较。我们发现，在所有罗萨斯和神速维度中，温暖和能力是对不同机器人行为之间人类偏好的最重要预测指标。即使没有明确的共识偏好或条件之间的显着因素差异，这种预测能力也具有。
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.
