Author
Kırtay, M., Öztop, Erhan, Kuhlen, A. K., asa, M., Hafner, V. V.
Publication Date
2022
Publication Place
-
IEEE
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-172818859-1
Record ID
536447f1-1f34-4ea2-9377-3053430db36d
Library Location
Computer Science
Date
2022
Notes
Deutsche Forschungsgemeinschaft
Sample Text
In this study, we extend our robot trust model into a multimodal setting in which the Nao robot leverages audio-visual data to perform a sequential multimodal pattern recalling task while interacting with a human partner who has different guiding strategies: reliable, unreliable, and random. Here, the humanoid robot is equipped with a multimodal auto-associative memory module to process audio-visual patterns to extract cognitive load (i.e., computational cost) and an internal reward module to perform cost-guided reinforcement learning. After interactive experiments, the robot associates a low cognitive load (i.e., high cumulative reward) yielded during the interaction with high trustworthiness of the guiding strategy of the partner. At the end of the experiment, we provide a free choice to the robot to select a trustworthy instructor. We show that the robot forms trust in a reliable partner. In the second setting of the same experiment, we endow the robot with an additional simple theory of mind module to assess the efficacy of the instructor in helping the robot perform the task. Our results show that the performance of the robot is improved when the robot bases its action decisions on factoring in the instructor assessment.
DOI
10.1109/RO-MAN53752.2022.9900730
