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The idea of robotic companions capable of establishing meaningful relationships with humans remains far from being accomplished. To achieve this, robots must interact with people in natural ways, employing social mechanisms that people use while interacting with each other. One such mechanism is empathy, often seen as the basis of social cooperation and prosocial behaviour. We argue that artificial companions capable of behaving in an empathic manner, which involves the capacity to recognise another's affect and respond appropriately, are more successful at establishing and maintaining a positive relationship with users. This paper presents a study where an autonomous robot with empathic capabilities acts as a social companion to two players in a chess game. The robot reacts to the moves played on the chessboard by displaying several facial expressions and verbal utterances, showing empathic behaviours towards one player and behaving neutrally towards the other. Quantitative and qualitative results of 31 participants indicate that users towards whom the robot behaved empathically perceived the robot as friendlier, which supports our hypothesis that empathy plays a key role in human-robot interaction.

The idea of autonomous social robots capable of assisting us in our daily lives is becoming more real every day. However, there are still many open issues regarding the social capabilities that those robots should have in order to make daily interactions with humans more natural. For example, the role of affective interactions is still unclear. This paper presents an ethnographic study conducted in an elementary school where 40 children interacted with a social robot capable of recognising and responding empathically to some of the children's affective states. The findings suggest that the robot's empathic behaviour affected positively how children perceived the robot. However, the empathic behaviours should be selected carefully, under the risk of having the opposite effect. The target application scenario and the particular preferences of children seem to influence the “degree of empathy” that social robots should be endowed with.

Empathy can be broadly defined as the ability to understand and respond appropriately to the affective states of others. In this paper, we present a scenario where a social robot acts as a chess companion for children, and describe our current efforts towards endowing such robot with empathic capabilities. A multimodal framework for modeling some of the user's affective states that combines visual and task-related features is presented. Using this model of the user, we personalise the learning environment by adapting the robot's empathic responses to the particular preferences of the child who is interacting with the robot. We also describe a preliminary study conducted in this scenario.

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.

Facing the challenge of accurately expressing emotions with robots and characters with limited expressive capabilities, we developed an abstraction model for generating emotional expressions based on the atomic features that enable human beings to recognize emotions in other humans' faces. The model is also augmented by animation theory from movies and puppetry. A small evaluation of the expressions showed that some expressions were well recognized, in particular Anger and Sadness.

For robots to become our personal companions in the future, they need to know how to socially interact with us. One defining characteristic of human social behaviour is empathy. In this paper, we present a robot that acts as a social companion expressing different kinds of empathic behaviours through its facial expressions and utterances. The robot comments the moves of two subjects playing a chess game against each other, being empathic to one of them and neutral towards the other. The results of a pilot study suggest that users to whom the robot was empathic perceived the robot more as a friend.

Empathy is a very important capability in human social relationships. If we aim to build artificial companions (agents or robots) capable of establishing long-term relationships with users, they should be able to understand the user's affective state and react accordingly, that is, behave in an empathic manner.Recent advances in affect recognition research show that it is possible to automatically analyse and interpret affective expressions displayed by humans. However, affect recognition in naturalistic environments is still a challenging issue and there are many unanswered questions related to how a virtual agent or a social robot should react to those states, and how that improves the interaction. We have developed a scenario in which a social robot recognises the user's affective state and displays empathic behaviours. In this paper, we present part of the results of a study assessing the influence of the robot's empathic behaviour on the user's understanding of the interaction.

The Inter-ACT (INTEracting with Robots - Affect Context Task) corpus is an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. It contains videos that capture the interaction from different perspectives and includes synchronised contextual information about the game and the behaviour displayed by the robot. The Inter-ACT corpus is mainly intended to be a comprehensive repository of naturalistic and contextualised, task-dependent data for the training and evaluation of an affect recognition system in an educational game scenario. The richness of contextual data that captures the whole human-robot interaction cycle, together with the fact that the corpus was collected in the same interaction scenario of the target application, make the Inter-ACT corpus unique in its genre.

The ability of artificial companions (virtual agents or robots) to establish meaningful relationships with users is still limited. In humans, a key aspect of such ability is empathy, often seen as the basis of social cooperation and pro-social behaviour. In this paper, we present a study where a social robot with empathic capabilities interacts with two users playing a chess game against each other. During the game, the agent behaves in an empathic manner towards one of the players and in a neutral way towards the other. In an experiment conducted with 40 participants, results showed that users to whom the robot was empathic provided higher ratings in terms of companionship.

Given the recent advances in robot and synthetic character technology, many researchers are now focused on ways of establishing social relations between these agents and humans over long periods of time. Early studies have shown that the novelty effect of robots and agents quickly wears out and that people change their attitudes and preferences towards them over time. In this paper, we study the role of social presence in long-term human-robot interactions. We conducted a study where children played chess exercises with a social robot over a five week period. With this experiment, we identified possible key issues that should be considered when designing social robots for long-term interactions.

Robot companions must be able to display social, affective behaviour. As a prerequisite for companionship, the ability to sustain long-term interactions with users requires companions to be endowed with affect recognition abilities. This paper explores application-dependent user states in a naturalistic scenario where an iCat robot plays chess with children. In this scenario, the role of context is investigated for the modelling of user states related both to the task and the social interaction with the robot. Results show that contextual features related to the game and the iCat's behaviour are successful in helping to discriminate among the identified states. In particular, state and evolution of the game and display of facial expressions by the iCat proved to be the most significant: when the user is winning and improving in the game her feeling is more likely to be positive and when the iCat displays a facial expression during the game the user's level of engagement with the iCat is higher. These findings will provide the foundation for a rigorous design of an affect recognition system for a game companion.

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.

In this paper we describe a robotic game buddy whose emotional behaviour is influenced by the state of the game. Using the iCat robot and chess as the game scenario, an architecture for incorporating emotions as a result of a heuristic evaluation of the state of the game was developed. The game buddy was evaluated in two ways. First, we investigated the effects of the characterpsilas emotional behaviour on the userpsilas perception of the game state. And secondly we compared a robotic with a screen based version of the iCat in terms of their influence on userpsilas enjoyment. The results suggested that userpsilas perception of the game increases with the iCatpsilas emotional behaviour, and that the enjoyment is higher when interacting with the robotic version.