  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.
