TY - GEN
T1 - Meta reinforcement learning based underwater manipulator control
AU - Moon, Jiyoun
AU - Bae, Sung-hoon
AU - Cashmore, Michael
N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2021/12/28
Y1 - 2021/12/28
N2 - 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.
AB - 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.
KW - manipulator control
KW - meta reinforcement learning
KW - model based reinforcement learning
KW - robotic manipulation
KW - underwater robot
UR - http://www.scopus.com/inward/record.url?scp=85124178476&partnerID=8YFLogxK
U2 - 10.23919/ICCAS52745.2021.9650009
DO - 10.23919/ICCAS52745.2021.9650009
M3 - Conference contribution book
AN - SCOPUS:85124178476
SN - 9781665418324
T3 - International Conference on Control, Automation and Systems
SP - 1473
EP - 1476
BT - 2021 21st International Conference on Control, Automation and Systems (ICCAS)
PB - IEEE
CY - Piscataway, N.J.
T2 - 21st International Conference on Control, Automation and Systems, ICCAS 2021
Y2 - 12 October 2021 through 15 October 2021
ER -