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

Bibliographic Data

ID8069614
AuthorsSebastian Loth (0000-0002-3580-8099, corresponding author), Katharina Jettka, Manuel Giuliani (0000-0003-3781-7623), Jan P De Ruiter (0000-0003-2897-8804)
Year2015
Volume6
Pages1641-1641
Publication date2015-11-04
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychology (JOURNAL)
Journal identifiersISSN: 1664-1078 • E-ISSN: 1664-1078
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyg.2015.01641
PMID26582998
OpenAlexW2155751166
LanguageEN
Citations received2
References cited64

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

Autism · Cognitive psychology · Human–computer interaction · Human–robot interaction · Joint attention · Mobile robot · Modalities · Redundancy (engineering · Robot · Robot control · Robustness (evolution · Social cue · Social relation · Social robot · AI in Service Interactions · Computer Science · Psychology · Social Robot Interaction and HRI · Speech and dialogue systems · Artificial Intelligence · Social Psychology

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