Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition benchmarks and directly applied to situated interactions. However, the conditions in situated human-robot interactions differ significantly from the training data and settings of these models. Thus, there is a need to deepen our understanding of how AC solutions can be best leveraged, customised and applied for situated HRI. This paper, while critiquing the existing practices, presents four critical lessons to be noted by the hitchhiker when applying AC for HRI research. These lessons conclude that: (i) The six basic emotions categories are irrelevant in situated interactions, (ii) Affect recognition accuracy (%) improvements are unimportant, (iii) Affect recognition does not generalise across contexts, and (iv) Affect recognition alone is insufficient for adaptation and personalisation. By describing the background and the context for each lesson, and demonstrating how these lessons have been learnt, this paper aims to enable the hitchhiker to successfully and insightfully leverage AC solutions for advancing HRI research.

翻译：社交机器人与人机交互（HRI）研究依赖于不同的情感计算（AC）解决方案，用以在交互过程中感知、察觉和理解人类情感行为。这可能包括利用现成的情感感知模型，这些模型在流行的情感识别基准上预训练，并直接应用于情境化交互。然而，情境化人机交互的条件与这些模型的训练数据和设置存在显著差异。因此，有必要加深我们对如何最优地利用、定制和应用AC解决方案以服务于情境化HRI的理解。本文在批判现有实践的同时，提出了搭便车者在将AC应用于HRI研究时应注意的四个关键教训。这些教训总结如下：（i）六种基本情感类别在情境化交互中不相关；（ii）情感识别准确率（%）的提升不重要；（iii）情感识别无法跨上下文泛化；（iv）仅靠情感识别不足以实现适应和个性化。通过描述每个教训的背景与情境，并展示这些教训是如何被习得的，本文旨在使搭便车者能够成功且富有洞察力地利用AC解决方案，以推动HRI研究的发展。