The social applications of robots possess intrinsic challenges with respect to social paradigms and heterogeneity of different groups. These challenges can be in the form of social acceptability, anthropomorphism, likeability, past experiences with robots etc. In this paper, we have considered a group of neurotypical adults to describe how different voices and motion types of the NAO robot can have effect on the perceived safety, anthropomorphism, likeability, animacy, and perceived intelligence of the robot. In addition, prior robot experience has also been taken into consideration to perform this analysis using a one-way Analysis of Variance (ANOVA). Further, we also demonstrate that these different modalities instigate different physiological responses in the person. This classification has been done using two different deep learning approaches, 1) Convolutional Neural Network (CNN), and 2) Gramian Angular Fields on the Blood Volume Pulse (BVP) data recorded. Both of these approaches achieve better than chance accuracy 25% for a 4 class classification.

翻译：机器人的社会应用在社会范式及不同群体的异质性方面存在固有挑战。这些挑战可能体现为社会接受度、拟人化程度、喜爱度、与机器人的过往接触经验等。本文以一组神经典型成年人为研究对象，探讨NAO机器人的不同语音和运动类型如何影响其感知安全性、拟人化程度、喜爱度、生命感及感知智能。同时，本文通过单因素方差分析（ANOVA）将先前机器人的接触经验纳入考虑进行分析。此外，我们进一步证明这些不同模态会引发个体不同的生理反应。该分类采用两种不同的深度学习方法实现：1）卷积神经网络（CNN），2）对采集的血容量脉冲（BVP）数据应用格拉姆角场。在4类分类任务中，两种方法的准确率均优于25%的概率基线。