A robot agent that learns group interaction through a team-based virtual reality game using affective reward reinforcement learning

Conference proceedings article

Authors/Editors

Strategic Research Themes

Publication Details

Author list: Chaichanawirote C., Tokumaru M., Charoenseang S.

Publisher: Springer

Publication year: 2020

Volume number: 1225 CCIS

Start page: 163

End page: 168

Number of pages: 6

ISBN: 9783030507282

ISSN: 18650929

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089243213&doi=10.1007%2f978-3-030-50729-9_22&partnerID=40&md5=227f996cf35a0507da0adfd84628baf4

Languages: English-Great Britain (EN-GB)

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Abstract

In the near future, robots are expected to be integrated into people’s lives, interacting with them. To develop better robotics and artificial intelligence, this research focuses on the concept of teamwork. A robot agent was implemented in a virtual reality(VR) game to play the sport roundnet, a team-based sport similar to table tennis and volleyball [2]. The agent is trained with reinforcement learning with EDA skin sensor data [6] of players. The system is evaluated using a questionnaire on the player’s feeling during the experiment and compared with agents not trained with affective data. The system is implemented in Unity3D’s ML-Agents Toolkit. © Springer Nature Switzerland AG 2020.

Keywords

Unity 3D, Virtual Reality