designing a game companion for long-term social interaction
The ability to understand the user's affective states and expressions is an important requirement for a robot companion. In this paper we focus on an interaction scenario where an iCat robot plays chess with children. We discuss the challenges to address in the design of an affect recognition system for a game companion and we present the initial steps carried out to build a framework for the dynamic modeling of the user's affect. We conclude by providing examples of how we intend to exploit information about the user's affect to generate an empathic behavior in our game companion.
affect recognition contextual information non-verbal behavior human-robot interaction