ABSTRACT

Personality estimation is a vital ability for communicating with others. It can help robots that interact with humans to model various human behaviors with a few parameters. Numerous studies have proposed models for estimating human personality from human–robot interaction. However, a limited number of methods have focused on the personalities of toddlers, which are dominated by innate temperaments. In this study, we propose a regression model that estimates the toddler temperament from images acquired by a teleoperated childcare robot named ChiCaRo. We gather a dataset from actual interactions between toddlers and ChiCaRo, and extract features from the data to train the regression model. Moreover, an explainable Artificial Intelligence model known as Shapley additive explanations (SHAP) is employed to understand the estimation tendency of the trained model and to compare the tendency with the temperament definition. The proposed model achieved a mean squared error of 0.024 for the average of all temperament factors. The analysis of SHAP confirmed that the model could reasonably learn the tendency compared to the definition in most temperament factors and suggested the possibility of data bias under a specific temperament factor.

摘要

人格评估是与他人交流的重要能力。它可以帮助与人类交互的机器人使用一些参数对各种人类行为进行建模。许多研究提出了通过人机交互来估计人类个性的模型。然而，有限数量的方法侧重于以先天气质为主的幼儿的个性。在这项研究中，我们提出了一个回归模型，该模型可以从名为 ChiCaRo 的遥控育儿机器人获取的图像中估计幼儿的气质。我们从幼儿和 ChiCaRo 之间的实际交互中收集数据集，并从数据中提取特征以训练回归模型。而且，一种可解释的人工智能模型，称为 Shapley 加性解释 (SHAP)，用于了解受训模型的估计趋势，并将该趋势与气质定义进行比较。对于所有气质因素的平均值，所提出的模型实现了 0.024 的均方误差。SHAP 的分析证实，该模型可以合理地学习与大多数气质因素的定义相比的趋势，并提出了特定气质因素下数据偏差的可能性。