Competitive physical interaction by reinforcement learning agents using intention estimation

Hiroki Noda, Satoshi Nishikawa, Ryuma Niiyama, Yasuo Kuniyoshi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

The physical human-robot interaction (pHRI) research field is expected to contribute to competitive and cooperative human-robot tasks that involve force interactions. However, compared with human-human interactions, current pHRI approaches lack tactical considerations. Current approaches do not estimate intentions from human behavior and do not select policies that are appropriate for the opponent's changing policy. For this reason, we propose a reinforcement learning model that estimates the opponent's changing policy using time-series observations and expresses the agent's policy in a common latent space, referring to descriptions of tactics in open-skill sports. We verify the performance of the reinforcement learning agent using two novel physical and competitive environments, push-hand game and air-hockey. From this, we confirm that the latent space works properly for policy information because each latent variable that represents the machine agent's own policy and that of the opponent affects the behavior of the agent. Two latent variables can clearly express how the agent estimates the opponent's policy and decides its own policy.

本文言語英語
ホスト出版物のタイトル2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ649-656
ページ数8
ISBN(電子版)9781665404921
DOI
出版ステータス出版済み - 8 8 2021
外部発表はい
イベント30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021 - Virtual, Vancouver, カナダ
継続期間: 8 8 20218 12 2021

出版物シリーズ

名前2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021

会議

会議30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
国/地域カナダ
CityVirtual, Vancouver
Period8/8/218/12/21

All Science Journal Classification (ASJC) codes

  • 人間とコンピュータの相互作用
  • 通信
  • 人工知能

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