Robots that express human’s social norms, like empathy, are perceived as more friendly, understanding, and caring. However, appropriate human-like empathic behaviors cannot be defined in advance, instead, they must be learned through daily interaction with humans in different situations. Additionally, to learn and apply the correct behaviors, robots must be able to perceive and understand the affective states of humans. This study presents a framework to enable cognitive empathy in social robots, which uses facial emotion recognition to perceive and understand the affective states of human users. The perceived affective state is then provided to a reinforcement learning model to enable a robot to learn the most appropriate empathic behaviors for different states. The proposed framework has been evaluated through an experiment between 28 individual humans and the humanoid robot Pepper. The results show that by applying empathic behaviors selected by the employed learning model, the robot is able to provide participants comfort and confidence and help them enjoy and feel better.

表示人类社会规范（例如同情心）的机器人被认为更加友好，理解和关怀。但是，不能预先定义适当的类似人的共情行为，而是必须通过与不同情况下的人的日常交互来学习。此外，要学习和应用正确的行为，机器人必须能够感知和理解人类的情感状态。这项研究提出了一个在社交机器人中实现认知共情的框架，该框架使用面部情感识别来感知和理解人类用户的情感状态。然后将感知到的情感状态提供给强化学习模型，以使机器人能够学习不同状态下最合适的共情行为。通过在28个人和仿人机器人Pepper之间进行的实验对提出的框架进行了评估。结果表明，通过应用所采用的学习模型选择的共情行为，该机器人能够为参与者提供舒适感和自信心，并帮助他们享受和感觉更好。