Detecting perceived quality of interaction with a robot using contextual features

This work aims to advance the state of the art in exploring the role of task, social context and their interdependencies in the automatic prediction of affective and social dimensions in human–robot interaction. We explored several SVMs-based models with different features extracted from a set of context logs collected in a human–robot interaction experiment where children play a chess game with a social robot. The features include information about the game and the social context at the interaction level (overall features) and at the game turn level (turn-based features). While overall features capture game and social context at the interaction level, turn-based features attempt to encode the dependencies of game and social context at each turn of the game. Results showed that game and social context-based features can be successfully used to predict dimensions of quality of interaction with the robot. In particular, overall features proved to perform equally or better than turn-based features, and game context-based features more effective than social context-based features. Our results show that the interplay between game and social context-based features, combined with features encoding their dependencies, lead to higher recognition performances for a subset of dimensions.