A®ect recognition for socially perceptive robots relies on representative data. While many of the existing a®ective corpora and databases contain posed and decontextualized a®ec-tive expressions, a®ect resources for designing an a®ect recognition system in naturalistic humanÀrobot interaction (HRI) must include context-rich expressions that emerge in the same scenario of the¯nal application. In this paper, we propose a context-based approach to the collection and modeling of representative data for building an a®ect-sensitive robotic game companion. To illustrate our approach we present the key features of the Inter-ACT (INTEracting with RobotsÀA®ect Context Task) corpus, an a®ective and contextually rich multimodal video corpus containing a®ective expressions of children playing chess with an iCat robot. We show how this corpus can be successfully used to train a context-sensitive a®ect recognition system (a valence detector) for a robotic game companion. Finally, we demonstrate how the integration of the a®ect recognition system in a modular platform for adaptive HRI makes the interaction with the robot more engaging.
In REF Castellano et al. presented a system that learned to perceive affective expressions of children playing chess with an iCat robot by processing their facial expressions.
MULTIMODAL AFFECT MODELING AND RECOGNITION FOR EMPATHIC ROBOT COMPANIONS
Int. J. Humanoid Robotics
Int. J. Humanoid Robotics
Computer Science