Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots

Strathearn, Carl and Ma, Minhua (2020) Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots. Informatics, 7 (3). p. 28. ISSN 2227-9709

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Abstract or description

Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable.

Item Type:	Article
Uncontrolled Keywords:	embodied artificial intelligence; realistic humanoid robots; human–robot interaction; human–computer interaction
Faculty:	School of Digital, Technologies and Arts > Computer Science, AI and Robotics
Depositing User:	Library STORE team
Date Deposited:	10 Aug 2020 15:28
Last Modified:	24 Feb 2023 13:59
URI:	https://eprints.staffs.ac.uk/id/eprint/6455

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