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

Loth, S., Jettka, K., Giuliani, M. and De Ruiter, J. P. (2015) Ghost-in-the-machine reveals human social signals for human-robot interaction. Frontiers in Psychology, 6 (1641). Available from: http://eprints.uwe.ac.uk/31008

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution 4.0.

1MB

Publisher's URL: https://doi.org/10.3389/fpsyg.2015.01641

Abstract/Description

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.

Item Type:Article
Additional Information:This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission
Uncontrolled Keywords:ghost-in-the-machine, human, social, signals, human-robot, interaction
Faculty/Department:Faculty of Environment and Technology > Department of Engineering Design and Mathematics
ID Code:31008
Deposited By: Dr M. Giuliani
Deposited On:20 Feb 2017 13:12
Last Modified:20 Feb 2017 19:41

Request a change to this item

Total Document Downloads in Past 12 Months

Document Downloads

Total Document Downloads

More statistics for this item...