Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user’s affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans’ emotions and replying to them appropriately. The proposed model continuously detects the affective states of a user based on facial expressions and generates desired, either parallel or reactive, empathic behaviors that are already adapted to the user’s personality. Users’ affective states are detected using a stacked autoencoder network that is trained and tested on the RAVDESS dataset. The overall proposed empathic model is verified throughout an experiment, where different emotions are triggered in participants and then empathic behaviors are applied based on proposed hypothesis. The results confirm the effectiveness of the proposed model in terms of related social and friendship concepts that participants perceived during interaction with the robot.

中文翻译：

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一种响应用户面部表情的自主认知移情模型

成功的社交机器人服务取决于机器人如何与用户互动。可以通过机器人对用户的情感状态做出适当反应的流畅、参与和类人的交互来获得有效的服务。本文提出了一种新颖的自动认知移情模型，ACEM，通过考虑人类的情绪并适当地回复它们，人形机器人可以实现更长、更投入的人机交互 (HRI)。所提出的模型基于面部表情持续检测用户的情感状态，并生成已经适应用户个性的期望的、平行的或反应性的移情行为。使用在 RAVDESS 数据集上训练和测试的堆叠式自动编码器网络检测用户的情感状态。在整个实验中验证了整体提出的移情模型，其中在参与者中触发不同的情绪，然后根据提出的假设应用移情行为。结果证实了所提出的模型在参与者与机器人交互过程中感知的相关社交和友谊概念方面的有效性。