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

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.Comment: 11 pages, 3 figures, 1 tabl

Designing an Affective Cognitive Architecture for Human-Humanoid Interaction

Robots involved in HRI should be able to adapt to their partners by learning to select autonomously the behaviors that maximize the pleasantness of the interaction for them. To this aim, affect could play two important roles: serve as perceptual input to infer the emotional status and reactions of the human partner; and act as internal motivation system for the robot, supporting reasoning and action selection. In this perspective, we propose to develop an affect-based architecture for the humanoid robot iCub with the purpose of fully autonomous personalized HRI. This base framework can be generalized to fit many different contexts -social, educational, collaborative and assistive - allowing for natural, long-term, and adaptive interaction