Meta reinforcement learning based underwater manipulator control

Tools

Moon, Jiyoun and Bae, Sung-hoon and Cashmore, Michael; (2021) Meta reinforcement learning based underwater manipulator control. In: 2021 21st International Conference on Control, Automation and Systems (ICCAS). International Conference on Control, Automation and Systems . IEEE, KOR, pp. 1473-1476. ISBN 9788993215212 (https://doi.org/10.23919/ICCAS52745.2021.9650009)

[thumbnail of Zhang-etal-CI-2022-Structure-from-motion-based-image-unwrapping-and-stitching]
Preview
 Text. Filename: Zhang_etal_CI_2022_Structure_from_motion_based_image_unwrapping_and_stitching.pdf
Accepted Author Manuscript
License: Strathprints license 1.0
Download (7MB)| Preview

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.

ORCID iDs

Moon, Jiyoun, Bae, Sung-hoon and Cashmore, Michael ORCID logoORCID: https://orcid.org/0000-0002-8334-4348;
CORE (COnnecting REpositories)