Christoph Bartneck

Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor

Teaching is inherently a social interaction between teacher and student. Despite this knowledge, many educational tools, such as vocabulary training programs, still model the interaction in a tutoring scenario as unidirectional knowledge transfer rather than a social dialog. Therefore, ongoing research aims to develop virtual agents as more appropriate media in education. Virtual agents can induce the perception of a life-like social interaction partner that communicates through natural modalities such as speech, gestures and emotional expressions. This effect can be additionally enhanced with a physical robotic embodiment. This paper presents the development of social supportive behaviors for a robotic tutor to be used in a language learning application. The effect of these behaviors on the learning performance of students was evaluated. The results support that employing social supportive behavior increases learning efficiency of students.

The influence of people’s culture and prior experiences with Aibo on their attitude towards robots

This paper presents a cross-cultural study on peoples’ negative attitude toward robots. 467 participants from seven different countries filled in the negative attitude towards robots scale survey which consists of 14 questions in three clusters: attitude towards the interaction with robots, attitude towards social influence of robots and attitude towards emotions in interaction with robots. Around one half of them were recruited at local universities and the other half was approached through Aibo online communities. The participants’ cultural background had a significant influence on their attitude and the Japanese were not as positive as stereotypically assumed. The US participants had the most positive attitude, while participants from Mexico had the most negative attitude. The participants from the online community were more positive towards robots than those not involved. Previous experience in interacting with Aibo also had a positive effect, but owning an Aibo did not improve their attitude.

Detecting h-index manipulation through self-citation analysis

The h-index has received an enormous attention for being an indicator that measures the quality of researchers and organizations. We investigate to what degree authors can inflate their h-index through strategic self-citations with the help of a simulation. We extended Burrell’s publication model with a procedure for placing self-citations, following three different strategies: random self-citation, recent self-citations and h-manipulating self-citations. The results show that authors can considerably inflate their h-index through self-citations. We propose the q-index as an indicator for how strategically an author has placed self-citations, and which serves as a tool to detect possible manipulation of the h-index. The results also show that the best strategy for an high h-index is publishing papers that are highly cited by others. The productivity has also a positive effect on the h-index.

A design-centred framework for social human-robot interaction

Robots currently integrate into our everyday lives, but little is known about how they can act socially. In this paper, we propose a definition of social robots and describe a framework that classifies properties of social robots. The properties consist of form, modality, social norms, autonomy, and interactivity. Finally, we provide broad guidelines for the design of social robots.

Perception of affect elicited by robot motion

Nonverbal behaviors serve as a rich source of information in inter human communication. In particular, motion cues can reveal details on a persons current physical and mental state. Research has shown, that people do not only interpret motion cues of humans in these terms, but also the motion of animals and inanimate devices such as robots. In order to successfully integrate mobile robots in domestic environments, designers have therefore to take into account how the device will be perceived by the user. In this study we analyzed the relationship between motion characteristics of a robot and perceived affect. Based on a literature study we selected two motion characteristics, namely acceleration and curvature, which appear to be most influential for how motion is perceived. We systematically varied these motion parameters and recorded participants interpretations in terms of affective content. Our results suggest a strong relation between motion parameters and attribution of affect, while the type of embodiment had no effect. Furthermore, we found that the level of acceleration can be used to predict perceived arousal and that valence information is at least partly encoded in an interaction between acceleration and curvature. These findings are important for the design of behaviors for future autonomous household robots.

Interacting with an embodied emotional character

A salient feature of the ambient intelligent home of the future will be the natural interaction between the home and its inhabitants through speech. An embodied home character is necessary to ensure a natural dialogue by continuously providing intuitive feedback in the form of conversational and emotional body language. This study experimentally investigates the influence of the characters embodiment (screen character and robotic character) and its emotional expressiveness on the enjoyability of the interaction. The presence of emotional expressions significantly increased the enjoyability of the interaction with the robotic character. The embodiment had no significant influence on the enjoyability. However, in the robotic character condition a social facilitation effect and a high forgiveness for speech recognition errors was observed.

Subtle emotional expressions of synthetic characters

This study examines the influence of the geometrical intensity of an emotional facial expression on the perceived intensity and the recognition accuracy. The stimuli consisted of synthetic faces at ten geometrical intensity levels in each of the five emotional categories. A curve-linear relationship was found between geometrical and perceived intensity. Steps of 20% geometrical intensity appear to be appropriate to enable the participants to distinguish the intensity levels. At about 30% geometrical intensity the recognition accuracy reached a level that was not significantly different from each emotions maximum recognition accuracy. This point indicates a categorical perception of the facial expressions. The results of this study are of particular importance for the developers of synthetic characters and might help them to create more subtle characters.

How Convincing is Mr. Data's Smile: Affective Expressions of Machines

Emotions should play an important role in the design of interfaces because people interact with machines as if they were social actors. This paper presents a literature review on affective expressions through speech, music and body language. It summarizes the quality and quantity of their parameters and successful examples of synthesis. Moreover, a model for the convincingness of affective expressions, based on Fogg and Hsiang Tseng (1999), was developed and tested. The empirical data did not support the original model and therefore this paper proposes a new model, which is based on appropriateness and intensity of the expressions. Furthermore, the experiment investigated if the type of emotion (happiness, sadness, anger, surprise, fear and disgust), knowledge about the source (human or machine), the level of abstraction (natural face, computer rendered face and matrix face) and medium of presentation (visual, audio/visual, audio) of an affective expression influences its convincingness and distinctness. Only the type of emotion and multimedia presentations had an effect on convincingness. The distinctness of an expression depends on the abstraction and the media through which it is presented.

Comparing the similarity of responses received from studies in Amazon's Mechanical Turk to studies conducted online and with direct recruitment.

Computer and internet based questionnaires have become a standard tool in Human-Computer Interaction research and other related fields, such as psychology and sociology. Amazon’s Mechanical Turk (AMT) service is a new method of recruiting participants and conducting certain types of experiments. This study compares whether participants recruited through AMT give different responses than participants recruited through an online forum or recruited directly on a university campus. Moreover, we compare whether a study conducted within AMT results in different responses compared to a study for which participants are recruited through AMT but which is conducted using an external online questionnaire service. The results of this study show that there is a statistical difference between results obtained from participants recruited through AMT compared to the results from the participant recruited on campus or through online forums. We do, however, argue that this difference is so small that it has no practical consequence. There was no significant difference between running the study within AMT compared to running it with an online questionnaire service. There was no significant difference between results obtained directly from within AMT compared to results obtained in the campus and online forum condition. This may suggest that AMT is a viable and economical option for recruiting participants and for conducting studies as setting up and running a study with AMT generally requires less effort and time compared to other frequently used methods. We discuss our findings as well as limitations of using AMT for empirical studies.

Does the Design of a Robot Influence Its Animacy and Perceived Intelligence

Robots exhibit life-like behavior by performing intelligent actions. To enhance human-robot interaction it is necessary to investigate and understand how end-users perceive such animate behavior. In this paper, we report an experiment to investigate how people perceived different designs of robot embodiments in terms of animacy and intelligence. iCat and Robovie II were used as the two embodiments in this experiment. We conducted a between-subject experiment where robot type was the independent variable, and perceived animacy and intelligence of the robot were the dependent variables. Our findings suggest that a robot’s perceived intelligence is significantly correlated with animacy. The correlation between the intelligence and the animacy of a robot was observed to be stronger in the case of the iCat embodiment. Our results also indicate that the more animated the face of the robot, the more likely it is to attract the attention of a user. We also discuss the possible and probable explanations of the results obtained.