Are emotional robots more fun to play with?

Leite, Iolanda; Pereira, Ana Isabel; Martinho, Carlos; Paiva, Ana

doi:10.1109/roman.2008.4600646

Cited by 100 publications

(51 citation statements)

References 11 publications

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“…Regarding the iCat, researchers have not only used it to examine the influence of perceived social abilities of a robot on a user's attitude towards and acceptance of the robot (Heerink et al 2006), but also used it to examine the effects of iCat's emotional behavior on users' perception (Leite et al 2008). Leite et al (2008) used the iCat robot and chess (with physical electronic chessboard that detects the board state and sends it to the computer) to develop a chess game platform, where the robot's affective state was influenced by every move the player made, which meant that users could know whether they had made a good or a bad move by looking at iCat's expressions.…”

Section: Discussionmentioning

confidence: 99%

“…Leite et al (2008) used the iCat robot and chess (with physical electronic chessboard that detects the board state and sends it to the computer) to develop a chess game platform, where the robot's affective state was influenced by every move the player made, which meant that users could know whether they had made a good or a bad move by looking at iCat's expressions. By investigating the effects of the robot's emotional behavior on the user's perception of the game state, they found that a social robot with emotional behavior could perform the task of helping users to understand a gaming situation better than a robot without emotional behavior.…”

Section: Discussionmentioning

confidence: 99%

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Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Towards Autonomous Robotic Systems

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In the field of human-robot interaction, socially interactive robots are often equipped with the ability to detect the affective states of users, the ability to express emotions through the use of synthetic facial expressions, speech and textual content, and the ability for imitating and social learning. Past work on creating robots that can make convincing emotional expressions has concentrated on the quality of those expressions, and on assessing people's ability to recognize them. Previous recognition studies presented the facial expressions of the robots in neutral contexts, without any strong emotional valence (e.g., emotionally valenced music or video). It is therefore worth empirically exploring whether observers' judgments of the facial cues of a robot would be affected by a surrounding emotional context. This thesis takes its inspiration from the contextual effects found on the interpretation of the expressions on human faces and computer avatars, and looks at the extent to which they also apply to the interpretation of the facial expressions of a mechanical robot head. The kinds of contexts that affect the recognition of robot emotional expressions, the circumstances under which such contextual effects occur, and the relationship between emotions and the surrounding situation, are observed and analyzed in a series of 11 experiments. In these experiments, the FACS (Facial Action Coding System) (Ekman and Friesen, 2002) was applied to set up the parameters of the servos to make the robot head produce sequences of facial expressions. Four different emotional surrounding or preceding contexts were used (i.e., recorded BBC News pieces, selected affective pictures, classical music pieces and film clips). This thesis provides evidence that observers' judgments about the facial expressions of a robot can be affected by a surrounding emotional context. From a psychological perspective, the contextual effects found on the robotic facial expressions based on the FACS, indirectly support the claims that human emotions are both 4 biologically based and socially constructed. From a robotics perspective, it is argued that the results obtained from the analyses will be useful for guiding researchers to enhance the expressive skills of emotional robots in a surrounding emotional context. This thesis also analyzes the possible factors contributing to the contextual effects found in the original 11 experiments.Some future work, including four new experiments (a preliminary experiment designed to identify appropriate contextual materials and three further experiments in which factors likely to affect a context effect are controlled one by one) is also proposed in this thesis.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Towards Autonomous Robotic Systems

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“…On the other hand, mood represents a background affective state, less intense but always present. The affective system is self-oriented or competitive, i.e., when the user plays a good move the iCat displays a sad facial expression and when the user plays a bad move the iCat displays positive reactions (for more details in the affective system please see [18]). We have adopted this approach instead of a more cooperative behaviour because, from our observations of children playing against each other in a chess club, such reactions are more consistent with what they might expect about their opponents.…”

Section: Ana Paiva (With Iolanda Leite)mentioning

confidence: 99%

Social interaction with robots and agents: Where do we stand, where do we go?

Hudlická¹,

Payr

Ventura

et al. 2009

2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops

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“…In [23], preliminary experiments on remote education have shown that students interacting with robots show pleasure and interest. [10] shows that children understand better the gaming situations when the robot shows emotional behaviors. Moreover, children seem to enjoy the game more when they play with embodied characters than when they play with screen-printed characters.…”

Section: Introductionmentioning

confidence: 97%

Maggie: A Social Robot as a Gaming Platform

González-Pacheco

Ramey

Alonso-Martín

et al. 2011

Int J of Soc Robotics

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Edutainment robots are robots designed to participate in people's education and in their entertainment. One of the tasks of edutainment robots is to play with their human partners, but most of them offer a limited pool of games. Moreover, it is difficult to add new games to them. This lack of flexibility could shorten their life cycle. This paper presents a social robot on which several robotic games have been developed. Our robot uses a flexible and modular architecture that allows the creation of new skills by the composition of existing and simpler skills. With this architecture, the development of a new game mainly consists in the composition of the skills that are needed for this specific game. In this paper, we present the robot, its hardware and its software architecture, including its interaction capabilities. We also provide a detailed description of the development of five of the games the robot can play.

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Are emotional robots more fun to play with?

Cited by 100 publications

References 11 publications

Contextual Recognition of Robot Emotions

Contextual Recognition of Robot Emotions

Social interaction with robots and agents: Where do we stand, where do we go?

Maggie: A Social Robot as a Gaming Platform

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