Training a Robotic Arm Movement with Deep Reinforcement Learning

Xiaohan Ni*, Xin He, Takafumi Matsumaru

*この研究の対応する著者

研究成果: Conference contribution

抄録

This paper introduces a general experimental design scheme for conditions and parameter settings of robotic arm control under the specific task when using Deep Deterministic Policy Gradient(DDPG) algorithm to train the robotic arm for completing the control task. Based on the Coppelia simulation tool, this paper builds an interactive reinforcement learning environment for robotic arm control tasks, and designs two different control tasks to verify the validity of experimental design schemes. Conclusions in this paper provide an important reference for finding suitable environmental design and parameter settings for using DDPG to train a manipulator and improving the training effect.

本文言語English
ホスト出版物のタイトル2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ595-600
ページ数6
ISBN(電子版)9781665405355
DOI
出版ステータスPublished - 2021
イベント2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 - Sanya, China
継続期間: 2021 12月 272021 12月 31

出版物シリーズ

名前2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

Conference

Conference2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
国/地域China
CitySanya
Period21/12/2721/12/31

ASJC Scopus subject areas

  • 人工知能
  • 機械工学
  • 制御と最適化

フィンガープリント

「Training a Robotic Arm Movement with Deep Reinforcement Learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル