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Social Robotics and Human-Robot Interaction (HRI) research relies ondifferent Affective Computing (AC) solutions for sensing, perceiving andunderstanding human affective behaviour during interactions. This may includeutilising off-the-shelf affect perception models that are pre-trained onpopular affect recognition benchmarks and directly applied to situatedinteractions. However, the conditions in situated human-robot interactionsdiffer 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 bestleveraged, customised and applied for situated HRI. This paper, whilecritiquing the existing practices, presents four critical lessons to be notedby the hitchhiker when applying AC for HRI research. These lessons concludethat: (i) The six basic emotions categories are irrelevant in situatedinteractions, (ii) Affect recognition accuracy (%) improvements areunimportant, (iii) Affect recognition does not generalise across contexts, and(iv) Affect recognition alone is insufficient for adaptation andpersonalisation. By describing the background and the context for each lesson,and demonstrating how these lessons have been learnt, this paper aims to enablethe hitchhiker to successfully and insightfully leverage AC solutions foradvancing HRI research.

Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker