Iolanda Leite

Social Robots for Long-Term Interaction: A Survey

As the field of HRI evolves, it is important to understand how users interact with robots over long periods. This paper reviews the current research on long-term interaction between users and social robots. We describe the main features of these robots and highlight the main findings of the existing long-term studies. We also present a set of directions for future research and discuss some open issues that should be addressed in this field.

Automatic analysis of affective postures and body motion to detect engagement with a game companion

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.

Detecting user engagement with a robot companion using task and social interaction-based features

Affect sensitivity is of the utmost importance for a robot companion to be able to display socially intelligent behaviour, a key requirement for sustaining long-term interactions with humans. This paper explores a naturalistic scenario in which children play chess with the iCat, a robot companion. A person-independent, Bayesian approach to detect the users engagement with the iCat robot is presented. Our framework models both causes and effects of engagement: features related to the users non-verbal behaviour, the task and the companions affective reactions are identified to predict the childrens level of engagement. An experiment was carried out to train and validate our model. Results show that our approach based on multimodal integration of task and social interaction-based features outperforms those based solely on non-verbal behaviour or contextual information (94.79 % vs. 93.75 % and 78.13 %).

Affect recognition for interactive companions: challenges and design in real world scenarios

Affect sensitivity is an important requirement for artificial companions to be capable of engaging in social interaction with human users. This paper provides a general overview of some of the issues arising from the design of an affect recognition framework for artificial companions. Limitations and challenges are discussed with respect to other capabilities of companions and a real world scenario where an iCat robot plays chess with children is presented. In this scenario, affective states that a robot companion should be able to recognise are identified and the non-verbal behaviours that are affected by the occurrence of these states in the children are investigated. The experimental results aim to provide the foundation for the design of an affect recognition system for a game companion: in this interaction scenario children tend to look at the iCat and smile more when they experience a positive feeling and they are engaged with the iCat.

Modelling empathic behaviour in a robotic game companion for children: an ethnographic study in real-world settings

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 childrens affective states. The findings suggest that the robots 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.

Empathic Robots for Long-term Interaction

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.

The influence of empathy in human-robot relations

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 anothers 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.

As Time goes by: Long-term evaluation of social presence in robotic companions

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.

It's all in the game: Towards an affect sensitive and context aware game companion

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 iCats 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 users 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.

Why can't we be friends? an empathic game companion for long-term interaction

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.