Modeling robot trust based on emergent emotion in an interactive task

Name: Modeling robot trust based on emergent emotion in an interactive task
Author: Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V.

İsim	Modeling robot trust based on emergent emotion in an interactive task
Yazar	Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V.
Basım Tarihi:	2021
Basım Yeri	- IEEE
Konu	Emotions, HRI, Internal reward, Trust, Visual recalling
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-172816242-3
Kayıt Numarası	fffe9f6a-e105-40f0-b2a7-51df24c62fca
Lokasyon	Computer Science
Tarih	2021
Notlar	Deutsche Forschungsgemeinschaft
Örnek Metin	Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing a computational model of robot trust in interaction partners remain relatively limited. In this study, we extend our emergent emotion model to propose that the robot's trust in the interaction partner (i.e., trustee) can be established by the effect of the interactions on the computational energy budget of the robot (i.e., trustor). To be concrete, we show how high-level emotions (e.g., wellbeing) of an agent can be modeled by the computational cost of perceptual processing (e.g., visual stimulus processing for visual recalling) in a decision-making framework. To realize this approach, we endow the Pepper humanoid robot with two modules: an auto-associative memory that extracts the required computational energy to perform a visual recalling, and an internal reward mechanism guiding model-free reinforcement learning to yield computational energy cost-aware behaviors. With this setup, the robot interacts with online instructors with different guiding strategies, namely reliable, less reliable, and random. Through interaction with the instructors, the robot associates the cumulative reward values based on the cost of perceptual processing to evaluate the instructors and determine which one should be trusted. Overall the results indicate that the robot can differentiate the guiding strategies of the instructors. Additionally, in the case of free choice, the robot trusts the reliable one that increases the total reward - and therefore reduces the required computational energy (cognitive load)- to perform the next task.
DOI	10.1109/ICDL49984.2021.9515645

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Modeling robot trust based on emergent emotion in an interactive task

Yazar Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V.

Basım Tarihi 2021

Basım Yeri - IEEE

Konu Emotions, HRI, Internal reward, Trust, Visual recalling

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-172816242-3

Kayıt Numarası fffe9f6a-e105-40f0-b2a7-51df24c62fca

Lokasyon Computer Science

Tarih 2021

Notlar Deutsche Forschungsgemeinschaft

Örnek Metin Trust is an essential component in human-human and human-robot interactions. The factors that play potent roles in these interactions have been an attractive issue in robotics. However, the studies that aim at developing a computational model of robot trust in interaction partners remain relatively limited. In this study, we extend our emergent emotion model to propose that the robot's trust in the interaction partner (i.e., trustee) can be established by the effect of the interactions on the computational energy budget of the robot (i.e., trustor). To be concrete, we show how high-level emotions (e.g., wellbeing) of an agent can be modeled by the computational cost of perceptual processing (e.g., visual stimulus processing for visual recalling) in a decision-making framework. To realize this approach, we endow the Pepper humanoid robot with two modules: an auto-associative memory that extracts the required computational energy to perform a visual recalling, and an internal reward mechanism guiding model-free reinforcement learning to yield computational energy cost-aware behaviors. With this setup, the robot interacts with online instructors with different guiding strategies, namely reliable, less reliable, and random. Through interaction with the instructors, the robot associates the cumulative reward values based on the cost of perceptual processing to evaluate the instructors and determine which one should be trusted. Overall the results indicate that the robot can differentiate the guiding strategies of the instructors. Additionally, in the case of free choice, the robot trusts the reliable one that increases the total reward - and therefore reduces the required computational energy (cognitive load)- to perform the next task.

DOI 10.1109/ICDL49984.2021.9515645