Training a Robotic Arm Movement with Deep Reinforcement Learning

Xiaohan Ni*, Xin He, Takafumi Matsumaru

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages595-600
Number of pages6
ISBN (Electronic)9781665405355
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021 - Sanya, China
Duration: 2021 Dec 272021 Dec 31

Publication series

Name2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021

Conference

Conference2021 IEEE International Conference on Robotics and Biomimetics, ROBIO 2021
Country/TerritoryChina
CitySanya
Period21/12/2721/12/31

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering
  • Control and Optimization

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