If robots are to successfully interact with humans, they need to measure, quantify and respond to the emotions we produce. Similar to humans, the perceptual cue inputs to any modelling that allows this will be based on behavioural expression and body activity features that are prototypical of each emotion. However, the likely employment of such robots in different cultures necessitates the tuning of the emotion feature recognition system to the specific feature profiles present in these cultures. The amount of tuning depends on the relative convergence of the cross-cultural mappings between the emotion feature profiles of the cultures where the robots will be used. The GRID instrument and the cognitive corpus linguistics methodology were used in a contrastive study analysing a selection of behavioural expression and body activity features to compare the feature profiles of joy, sadness, fear and anger within and between Polish and British English. The intra-linguistic differences that were found in the profile of emotion features suggest that weightings based on this profile can be used in robotic modelling to create emotion-sensitive socially interacting robots. Our cross-cultural results further indicate that this profile of features needs to be tuned in robots to make them emotionally competent in different cultures.

中文翻译：

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情感机器人：文化原型的建模和测试。

如果机器人要与人类成功互动，则需要测量，量化并响应我们产生的情感。与人类相似，任何允许进行建模的感知提示输入都将基于行为表达和身体活动特征，这些特征和行为是每种情感的原型。但是，在不同文化中使用此类机器人的可能性很大，因此有必要将情绪特征识别系统调整为这些文化中存在的特定特征配置文件。调整的数量取决于使用机器人的文化的情感特征档案之间的跨文化映射的相对收敛。GRID仪器和认知语料库语言学方法用于对比研究中，分析了行为表达和身体活动特征的选择，以比较波兰英语和英国英语之间以及其中的喜悦，悲伤，恐惧和愤怒的特征。在情绪特征的轮廓中发现的语言内差异表明，基于此轮廓的权重可用于机器人建模，以创建对情感敏感的社交互动机器人。我们的跨文化结果进一步表明，需要在机器人中调整此特征的轮廓，以使其在不同文化中具有情感上的称职能力。在情绪特征的轮廓中发现的语言内差异表明，基于此轮廓的权重可用于机器人建模，以创建对情感敏感的社交互动机器人。我们的跨文化结果进一步表明，需要在机器人中调整此特征的轮廓，以使其在不同文化中具有情感上的称职能力。在情绪特征的轮廓中发现的语言内差异表明，基于此轮廓的权重可用于机器人建模，以创建对情感敏感的社交互动机器人。我们的跨文化结果进一步表明，需要在机器人中调整此特征的轮廓，以使其在不同文化中具有情感上的称职能力。