Human facial and bodily expressions play a crucial role in human–human interaction to convey the communicator’s feelings. Being echoed by the influence of human social behavior, recent studies in human–robot interaction (HRI) have investigated how to generate emotional behaviors for social robots. Emotional behaviors can enhance user engagement, allowing the user to interact with robots in a transparent manner. However, they are ambiguous and affected by many factors, such as personality traits, cultures, and environments. This article focuses on developing the robot’s emotional bodily expressions adopting the user’s affective gestures. We propose the behavior selection and transformation model, enabling the robots to incrementally learn from the user’s gestures, to select the user’s habitual behaviors, and to transform the selected behaviors into robot motions. The experimental results under several scenarios showed that the proposed incremental learning model endows a social robot with the capability of entering into a positive, long-lasting HRI. We have also confirmed that the robot can express emotions through the imitated motions of the user. The robot’s emotional gestures that reflected the interacting partner’s traits were widely accepted within the same cultural group, and perceptible across different cultural groups in different ways.

中文翻译：

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通过人际互动学习社交机器人情感的身体表达

人的面部表情和身体表情在人与人之间的交流中扮演着至关重要的角色，传达了沟通者的感受。人类社会行为的影响呼应了人类与机器人互动（HRI）的最新研究，研究了如何为社交机器人产生情感行为。情绪行为可以增强用户的参与度，从而允许用户以透明的方式与机器人进行交互。但是，它们是模棱两可的，并受许多因素的影响，例如人格特质，文化和环境。本文重点研究采用用户的情感手势来开发机器人的情感身体表情。我们提出了行为选择和转换模型，使机器人能够从用户的手势中逐步学习，以选择用户的习惯性行为，并将选定的行为转换为机器人动作。在几种情况下的实验结果表明，所提出的增量学习模型赋予社交机器人进入积极，持久的HRI的能力。我们还证实了机器人可以通过用户的模仿动作来表达情绪。反映交互伙伴特征的机器人情感手势在同一个文化群体中被广泛接受，并且在不同文化群体中以不同的方式被感知。