Alle Publikationen
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2017
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(2017): Don’t stare at me: The impact of a humanoid robot’s gaze upon trust during a cooperative human–robot visual task. In: International Journal of Social Robotics 9 (5), S. 745-753. DOI: 10.1007/s12369-017-0422-y
DOI: https://doi.org/10.1007/s12369-017-0422-y Abstract: Gaze is an important tool for social communication. Gaze can influence trust, likability, and compliance. However, excessive gaze in some contexts can signal threat, dominance and aggression, and hence complex social rules govern the appropriate use of gaze. Using a between-subjects design we investigated the impact of three levels of robot gaze (averted, constant and ’situational’) upon participants’ likelihood of trusting a humanoid robot’s opinion in a cooperative visual tracking task. The robot, acting as a confederate, would disagree with participants’ responses on certain trials, and suggest a different answer. As constant, staring gaze between strangers is associated with dominance and threat, and averted gaze is associated with lying, we predicted participants would be most likely to be persuaded by a robot which only gazed during disagreements (’situational gaze’). However, gender effects were found, with females least likely to trust a robot which stared at them, and no significant differences between averted gaze and situational gaze. Implications and future work are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved)
2015
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(2015): Evaluating the engagement with social robots. In: International Journal of Social Robotics 7 (4), S. 465-478. DOI: 10.1007/s12369-015-0298-7
DOI: https://doi.org/10.1007/s12369-015-0298-7 Abstract: To interact and cooperate with humans in their daily-life activities, robots should exhibit human-like 'intelligence'. This skill will substantially emerge from the interconnection of all the algorithms used to ensure cognitive and interaction capabilities. While new robotics technologies allow us to extend such abilities, their evaluation for social interaction is still challenging. The quality of a human–robot interaction can not be reduced to the evaluation of the employed algorithms: we should integrate the engagement information that naturally arises during interaction in response to the robot’s behaviors. In this paper we want to show a practical approach to evaluate the engagement aroused during interactions between humans and social robots. We will introduce a set of metrics useful in direct, face to face scenarios, based on the behaviors analysis of the human partners. We will show how such metrics are useful to assess how the robot is perceived by humans and how this perception changes according to the behaviors shown by the social robot. We discuss experimental results obtained in two human-interaction studies, with the robots Nao and iCub respectively. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
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