Author
Kırtay, M., Öztop, Erhan, Asada, M., Hafner, V. V.
Publication Date
2021
Publication Place
-
IEEE
Subject
Emotions, HRI, Internal reward, Trust, Visual recalling
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-172816242-3
Record ID
fffe9f6a-e105-40f0-b2a7-51df24c62fca
Library Location
Computer Science
Date
2021
Notes
Deutsche Forschungsgemeinschaft
Sample Text
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
