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A research to construct a predictive-primitive for the motion sequence of an embodied system

Research Project

Project/Area Number 18K18128
Research Category

Grant-in-Aid for Early-Career Scientists

Allocation TypeMulti-year Fund
Review Section Basic Section 61050:Intelligent robotics-related
Research InstitutionThe University of Tokyo

Principal Investigator

Yonekura Shogo  東京大学, 大学院情報理工学系研究科, 特任研究員 (60456192)

Project Period (FY) 2018-04-01 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Keywords自己組織化 / スパイキングニューロン / ノイズ / 筋骨格系 / 予測モデル / 予測性 / 予測プリミティブ / reservoir computation / 運動プリミティブ / Physical reservoir
Outline of Final Research Achievements

Building a system to predict the motion trajectory of an embodied system consisting a many degrees-of-freedom (D.O.F.s), such as a robot, is in general difficult because the interaction of D.O.F.s. We find that a motion controlling system consisting of a stochastically-spiking neural network (sSSN) can provide the noise-induced self-organization effect to an embodied system, and therefore can improve the predictability of the motion. Based on this finding, we tested the sSSN using several embodied agents such as musculoskeletal biped robot and toy problems. We would like to conclude that our work implicates self-organization capability provided by stochastically-spiking neurons is a key factor to realize a motion-predictive system.

Academic Significance and Societal Importance of the Research Achievements

近年、モデルベースド強化学習や最適制御などの研究において予測モデルの構築さえ可能であれば柔軟に最適な運動を生成可能である事が示唆されているが、しかしながら、予測学習の困難さは依然として大きな問題である。本研究では、生物が広く利用している確率的発火パターンを示すスパイキングニューロンを用いる事によって、環境-身体ダイナミクスにおいて自己組織化が起こり、自由度凍結に似た効果が引き起こされ、結果、より容易に予測学習が実現できる可能性を示した。この研究によって、生体神経の機能の新しく深い理解が得られたとともに、スパイキングニューロンを用いた深層学習の新しい可能性が開かれたと期待される。

Report

(4 results)
  • 2020 Annual Research Report   Final Research Report ( PDF )
  • 2019 Research-status Report
  • 2018 Research-status Report
  • Research Products

    (2 results)

All 2020

All Journal Article (2 results) (of which Peer Reviewed: 2 results,  Open Access: 2 results)

  • [Journal Article] Spike-induced ordering: Stochastic neural spikes provide immediate adaptability to the sensorimotor system2020

    • Author(s)
      Yonekura Shogo、Kuniyoshi Yasuo
    • Journal Title

      Proceedings of the National Academy of Sciences

      Volume: 117 Issue: 22 Pages: 12486-12496

    • DOI

      10.1073/pnas.1819707117

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access
  • [Journal Article] Spiking Neurons Ensemble for Movement Generation in Dynamically Changing Environments2020

    • Author(s)
      Favier Kaname、Yonekura Shogo、Kuniyoshi Yasuo
    • Journal Title

      Conference: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

      Volume: na Pages: 3789-3794

    • DOI

      10.1109/iros45743.2020.9340721

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Open Access

URL: 

Published: 2018-04-23   Modified: 2022-01-27  

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