Fernández-Rodicio Enrique, Castro-González Álvaro, Gamboa-Montero Juan José, Carrasco-Martínez Sara, Salichs Miguel A
RoboticsLab, Department of Systems Engineering and Automation, Universidad Carlos III de Madrid, Av. de la Universidad 30, 28911 Madrid, Spain.
Sensors (Basel). 2024 Jun 5;24(11):3671. doi: 10.3390/s24113671.
Robots are becoming an increasingly important part of our society and have started to be used in tasks that require communicating with humans. Communication can be decoupled in two dimensions: symbolic (information aimed to achieve a particular goal) and spontaneous (displaying the speaker's emotional and motivational state) communication. Thus, to enhance human-robot interactions, the expressions that are used have to convey both dimensions. This paper presents a method for modelling a robot's expressiveness as a combination of these two dimensions, where each of them can be generated independently. This is the first contribution of our work. The second contribution is the development of an expressiveness architecture that uses predefined multimodal expressions to convey the symbolic dimension and integrates a series of modulation strategies for conveying the robot's mood and emotions. In order to validate the performance of the proposed architecture, the last contribution is a series of experiments that aim to study the effect that the addition of the spontaneous dimension of communication and its fusion with the symbolic dimension has on how people perceive a social robot. Our results show that the modulation strategies improve the users' perception and can convey a recognizable affective state.
机器人正日益成为我们社会的重要组成部分，并已开始应用于需要与人类交流的任务中。交流可以在两个维度上进行分解：符号性交流（旨在实现特定目标的信息）和自发性交流（展现说话者的情感和动机状态）。因此，为了增强人机交互，所使用的表达方式必须同时传达这两个维度。本文提出了一种将机器人的表现力建模为这两个维度组合的方法，其中每个维度都可以独立生成。这是我们工作的第一个贡献。第二个贡献是开发了一种表现力架构，该架构使用预定义的多模态表达方式来传达符号维度，并集成了一系列用于传达机器人情绪和情感的调制策略。为了验证所提出架构的性能，最后一个贡献是进行了一系列实验，旨在研究添加交流的自发性维度及其与符号维度的融合对人们如何感知社交机器人的影响。我们的结果表明，调制策略改善了用户的感知，并能够传达可识别的情感状态。
