In this paper we address the problem of multi-cue affect recognition in challenging environments such as child-robot interaction. Towards this goal we propose a method for automatic recognition of affect that leverages body expressions alongside facial expressions, as opposed to traditional methods that usually focus only on the latter. We evaluate our methods on a challenging child-robot interaction database of emotional expressions, as well as on a database of emotional expressions by actors, and show that the proposed method achieves significantly better results when compared with the facial expression baselines, can be trained both jointly and separately, and offers us computational models for both the individual modalities, as well as for the whole body emotion.
This research investigates the meaning of “human-computer relationship” and presents techniques for constructing, maintaining, and evaluating such relationships, based on research in social psychology, sociolinguistics, communication and other social sciences. Contexts in which relationships are particularly important are described, together with specific benefits (like trust) and task outcomes (like improved learning) known to be associated with relationship quality. We especially consider the problem of designing for long-term interaction, and define relational agents as computational artifacts designed to establish and maintain long-term social-emotional relationships with their users. We construct the first such agent, and evaluate it in a controlled experiment with 101 users who were asked to interact daily with an exercise adoption system for a month. Compared to an equivalent task-oriented agent without any deliberate social-emotional or relationship-building skills, the relational agent was respected more, liked more, and trusted more, even after four weeks of interaction. Additionally, users expressed a significantly greater desire to continue working with the relational agent after the termination of the study. We conclude by discussing future directions for this research together with ethical and other ramifications of this work for HCI designers. As a great number of robotic products are entering people’s lives, the question of how can they behave in order to sustain long-term interactions with users becomes increasingly more relevant. In this paper, we present an empathic model for social robots that aim to interact with children for extended periods of time. The application of this model to a scenario where a social robot plays chess with children is described. To evaluate the proposed model, we conducted a long-term study in an elementary school and measured children’s perception of social presence, engagement and social support. We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction. The design of an affect recognition system for socially perceptive robots relies on representative data: human-robot interaction in naturalistic settings requires an affect recognition system to be trained and validated with contextualised affective expressions, that is, expressions that emerge in the same interaction scenario of the target application. In this paper we propose an initial computational model to automatically analyse human postures and body motion to detect engagement of children playing chess with an iCat robot that acts as a game companion. Our approach is based on vision-based automatic extraction of expressive postural features from videos capturing the behaviour of the children from a lateral view. An initial evaluation, conducted by training several recognition models with contextualised affective postural expressions, suggests that patterns of postural behaviour can be used to accurately predict the engagement of the children with the robot, thus making our approach suitable for integration into an affect recognition system for a game companion in a real world scenario. Affect recognition for socially perceptive robots relies on representative data. While many of the existing affective corpora and databases contain posed and decontextualized affective expressions, affect resources for designing an affect recognition system in naturalistic human–robot interaction (HRI) must include context-rich expressions that emerge in the same scenario of the final application. In this paper, we propose a context-based approach to the collection and modeling of representative data for building an affect-sensitive robotic game companion. To illustrate our approach we present the key features of the Inter-ACT (INTEracting with Robots–Affect Context Task) corpus, an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. We show how this corpus can be successfully used to train a context-sensitive affect recognition system (a valence detector) for a robotic game companion. Finally, we demonstrate how the integration of the affect recognition system in a modular platform for adaptive HRI makes the interaction with the robot more engaging.