Meta reinforcement learning based underwater manipulator control

Jiyoun Moon, Sung-hoon Bae, Michael Cashmore

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

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Abstract

Robots have garnered significant attention owing to their advantages in terms of replacing human labor under hazardous environments. In particular, because underwater construction robots can perform various tasks that are highly dangerous under deep sea environments, the development of manipulator control technology for these underwater robots is crucial. In this study, we therefore introduce an underwater manipulator control method based on meta reinforcement learning. Specifically, we construct a real-world underwater robot manipulator environment using ROS Gazebo and conduct simulations for the testing and verification of the proposed method.

Original languageEnglish
Title of host publication2021 21st International Conference on Control, Automation and Systems (ICCAS)
Place of PublicationPiscataway, N.J.
PublisherIEEE
Pages1473-1476
Number of pages4
ISBN (Electronic)9788993215212
ISBN (Print)9781665418324
DOIs
Publication statusPublished - 28 Dec 2021
Event21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of
Duration: 12 Oct 202115 Oct 2021

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2021-October
ISSN (Print)1598-7833

Conference

Conference21st International Conference on Control, Automation and Systems, ICCAS 2021
Country/TerritoryKorea, Republic of
CityJeju
Period12/10/2115/10/21

Keywords

  • manipulator control
  • meta reinforcement learning
  • model based reinforcement learning
  • robotic manipulation
  • underwater robot

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