Emergence of Motor Synergy in Multi-directional Reaching with Deep Reinforcement Learning

Jihui Han, Jiazheng Chai, Mitsuhiro Hayashibe

研究成果: Conference contribution

抄録

In this study, we apply Deep Reinforcement Learning for handling full-dimensional 7 degrees of freedom arm reaching, and demonstrate the relations among motion error, energy, and synergy emergence during the learning process, to reveal the mechanism of employing motor synergy. Although synergy information has never been encoded into the reward function, the synergy effect naturally emerges, leading to a similar situation as human motion learning. To the best of our knowledge, this is a pioneer study verifying a concurrent relation between the error-energy index and synergy development in DRL for multi-directional reaching tasks. In addition, our proposed feedback-augmented DRL controller shows better capability over DRL only in terms of error-energy index.

本文言語English
ホスト出版物のタイトル2021 IEEE/SICE International Symposium on System Integration, SII 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ78-82
ページ数5
ISBN(電子版)9781728176581
DOI
出版ステータスPublished - 2021 1月 11
イベント2021 IEEE/SICE International Symposium on System Integration, SII 2021 - Virtual, Iwaki, Fukushima, Japan
継続期間: 2021 1月 112021 1月 14

出版物シリーズ

名前2021 IEEE/SICE International Symposium on System Integration, SII 2021

Conference

Conference2021 IEEE/SICE International Symposium on System Integration, SII 2021
国/地域Japan
CityVirtual, Iwaki, Fukushima
Period21/1/1121/1/14

ASJC Scopus subject areas

  • 人工知能
  • 情報システム
  • 情報システムおよび情報管理
  • 制御およびシステム工学
  • 産業および生産工学
  • 機械工学

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