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Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework

Title	Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework
Author	Taliaronak, V., Lange, A. L., Kırtay, M., Öztop, Erhan
Publication Date:	2023
Publication Place	- IEEE
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	979-8-3503-3670-2
Record ID	8ef04984-899f-4403-a023-789b4e3bdf3b
Library Location	Computer Science
Date	2023
Notes	German Research Foundation (DFG) ; International Joint Research Promotion Program, Osaka University ; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science
Sample Text	Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective - that is, forming robot trust in interaction partners. Our previous robot trust model relies on assessing the trustworthiness of the interaction partners based on the computational cognitive load incurred during the interactive task [1]. However, this model does not take into account the social markers indicative of trustworthiness, such as the gestures displayed by a human partner. In this study, we make a step toward this point by extending the model by integrating a social cue processing module to achieve social human-robot interaction. This new model serves as a novel social cognitive trust framework to enable the Pepper robot to evaluate the trustworthiness of its interaction partners based on both cognitive load (i.e., the cost of perceptual processing) and social cues (i.e., their gestures). For evaluating the efficacy of the framework, the Pepper robot with the developed model is put to interact with human partners who may take the roles of a reliable, unreliable, deceptive, or random suggestion providing partner. Overall, the results indicate that the proposed framework allows the Pepper robot to differentiate the guiding strategies of the partners by detecting deceptive partners and thus select a trustworthy partner in case of a free choice to perform the next task.
DOI	10.1109/RO-MAN57019.2023.10309519

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Advancing humanoid robots for social integration: Evaluating trustworthiness through a social cognitive framework

Author Taliaronak, V., Lange, A. L., Kırtay, M., Öztop, Erhan

Publication Date 2023

Publication Place - IEEE

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 979-8-3503-3670-2

Record ID 8ef04984-899f-4403-a023-789b4e3bdf3b

Library Location Computer Science

Date 2023

Notes German Research Foundation (DFG) ; International Joint Research Promotion Program, Osaka University ; Ministry of Education, Culture, Sports, Science and Technology, Japan (MEXT) Japan Society for the Promotion of Science

Sample Text Trust is an essential concept for human-human and human-robot interactions. Yet only a few studies have addressed this concept from a robot perspective - that is, forming robot trust in interaction partners. Our previous robot trust model relies on assessing the trustworthiness of the interaction partners based on the computational cognitive load incurred during the interactive task [1]. However, this model does not take into account the social markers indicative of trustworthiness, such as the gestures displayed by a human partner. In this study, we make a step toward this point by extending the model by integrating a social cue processing module to achieve social human-robot interaction. This new model serves as a novel social cognitive trust framework to enable the Pepper robot to evaluate the trustworthiness of its interaction partners based on both cognitive load (i.e., the cost of perceptual processing) and social cues (i.e., their gestures). For evaluating the efficacy of the framework, the Pepper robot with the developed model is put to interact with human partners who may take the roles of a reliable, unreliable, deceptive, or random suggestion providing partner. Overall, the results indicate that the proposed framework allows the Pepper robot to differentiate the guiding strategies of the partners by detecting deceptive partners and thus select a trustworthy partner in case of a free choice to perform the next task.

DOI 10.1109/RO-MAN57019.2023.10309519