Author
Kırtay, M., Hafner, V. V., Asada, M., Kuhlen, A. K., Öztop, Erhan
Publication Date
2022
Publication Place
-
IEEE
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
979-835030979-9
Record ID
af867d1e-d7fc-46a1-a5c7-ee9d403c6f14
Library Location
Computer Science
Date
2022
Notes
Deutsche Forschungsgemeinschaft ; Japan Society for the Promotion of Science ; Osaka University
Sample Text
Successful and efficient teamwork requires knowledge of the individual team members' expertise. Such knowledge is typically acquired in social interaction and forms the basis for socially intelligent, partner-Adapted behavior. This study aims to implement this ability in teams of multiple humanoid robots. To this end, a humanoid robot, Nao, interacted with three Pepper robots to perform a sequential audio-visual pattern recall task that required integrating multimodal information. Nao outsourced its decisions (i.e., action selections) to its robot partners to perform the task efficiently in terms of neural computational cost by applying reinforcement learning. During the interaction, Nao learned its partners' specific expertise, which allowed Nao to turn for guidance to the partner who has the expertise corresponding to the current task state. The cognitive processing of Nao included a multimodal auto-Associative memory that allowed the determination of the cost of perceptual processing (i.e., cognitive load) when processing audio-visual stimuli. In turn, the processing cost is converted into a reward signal by an internal reward generation module. In this setting, the learner robot Nao aims to minimize cognitive load by turning to the partner whose expertise corresponds to a given task state. Overall, the results indicate that the learner robot discovers the expertise of partners and exploits this information to execute its task with low neural computational cost or cognitive load.
DOI
10.1109/Humanoids53995.2022.10000205
