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Influence of Variable Environments and Character-Specific Design on Perception of Virtual Robots with Affective Labels

机译:可变环境和特定角色设计对带有情感标签的虚拟机器人感知的影响

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As robotic systems move out of the factory, it is essential that they adapt to changing environments. Distinct contextual settings may cause users to have a different perception of these systems. This paper explores the relationship between context and expression by looking at various combinations of robot characters and environments in a virtual setting. The robots are designed after characters from pop-culture. Physical traits such as color, eye shape, and motion profiles are abstractly analyzed and infused onto the robot. Six different robot characters and six different environments are used to see if individuals perceive each robot differently in varying environments. Additionally, the effect of character-specific priming as a context generator is also surveyed. It is found that the character with an affect label with positive valence was rated more favorably than characters with negative valence affect labels in positive valence environments and vice versa is true for environments with negative valence associated. Qualitative feedback from participants provides a meaningful description for each rating choice. This study shows the importance of contextual realizations while designing robotic systems that can be considered in future work looking at user perception.
机译:随着机器人系统移出工厂,必须适应不断变化的环境。不同的上下文设置可能会使用户对这些系统有不同的认识。本文通过在虚拟环境中查看机器人角色和环境的各种组合来探索上下文与表达之间的关系。机器人是根据流行文化中的角色设计的。诸如颜色,眼睛形状和动作轮廓之类的身体特征被抽象地分析并注入到机器人中。使用六个不同的机器人角色和六个不同的环境来查看个人在不同环境中对每个机器人的感知是否不同。此外,还调查了特定字符启动作为上下文生成器的效果。发现在正价环境中带有正价影响标签的字符比具有负价影响标签的字符更有利,反之亦然。参与者的定性反馈为每个评级选择提供了有意义的描述。这项研究表明了在设计机器人系统时考虑到上下文实现的重要性,这些系统可以在未来的工作中着眼于用户感知。

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