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Ghost-in-the-Machine reveals human social signals for human-robot interaction

Loth, Sebastian; Jettka, Katharina; Giuliani, Manuel; De Ruiter, Jan P

Authors

Sebastian Loth

Katharina Jettka

Manuel Giuliani Manuel.Giuliani@uwe.ac.uk
Professor in Embedded Cognitive AI for Robotics

Jan P De Ruiter



Abstract

© 2015 Loth, Jettka, Giuliani and de Ruiter. We used a new method called "Ghost-in-the-Machine" (GiM) to investigate social interactions with a robotic bartender taking orders for drinks and serving them. Using the GiM paradigm allowed us to identify how human participants recognize the intentions of customers on the basis of the output of the robotic recognizers. Specifically, we measured which recognizer modalities (e.g., speech, the distance to the bar) were relevant at different stages of the interaction. This provided insights into human social behavior necessary for the development of socially competent robots. When initiating the drink-order interaction, the most important recognizers were those based on computer vision. When drink orders were being placed, however, the most important information source was the speech recognition. Interestingly, the participants used only a subset of the available information, focussing only on a few relevant recognizers while ignoring others. This reduced the risk of acting on erroneous sensor data and enabled them to complete service interactions more swiftly than a robot using all available sensor data. We also investigated socially appropriate response strategies. In their responses, the participants preferred to use the same modality as the customer's requests, e.g., they tended to respond verbally to verbal requests. Also, they added redundancy to their responses, for instance by using echo questions. We argue that incorporating the social strategies discovered with the GiM paradigm in multimodal grammars of human-robot interactions improves the robustness and the ease-of-use of these interactions, and therefore provides a smoother user experience.

Journal Article Type Article
Publication Date Jan 1, 2015
Journal Frontiers in Psychology
Electronic ISSN 1664-1078
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 6
Issue NOV
Institution Citation Loth, S., Jettka, K., Giuliani, M., & De Ruiter, J. P. (2015). Ghost-in-the-Machine reveals human social signals for human-robot interaction. Frontiers in Psychology, 6(NOV), https://doi.org/10.3389/fpsyg.2015.01641
DOI https://doi.org/10.3389/fpsyg.2015.01641
Keywords ghost-in-the-machine, human, social, signals, human-robot, interaction
Publisher URL https://doi.org/10.3389/fpsyg.2015.01641
Related Public URLs http://journal.frontiersin.org/article/10.3389/fpsyg.2015.01641/full
Additional Information Additional Information : This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission

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