A reinforcement learning control approach for underwater manipulation under position and torque constraints

Ignacio Carlucho, Mariano De Paula, Corina Barbalata, Gerardo G. Acosta

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

Abstract / Description of output

In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, low-level control methods require great capabilities to adapt to change. Furthermore, under position and torque constraints the requirements for the control system are greatly increased. Reinforcement learning is a data driven control technique that can learn complex control policies without the need of a model. The learning capabilities of these type of agents allow for great adaptability to changes in the operative conditions. In this article we present a novel reinforcement learning low-level controller for the position control of an underwater manipulator under torque and position constraints. The reinforcement learning agent is based on an actor-critic architecture using sensor readings as state information. Simulation results using the Reach Alpha 5 underwater manipulator show the advantages of the proposed control strategy.
Original languageEnglish
Title of host publicationGlobal Oceans 2020: Singapore – U.S. Gulf Coast
PublisherIEEE
Pages1-7
Number of pages7
ISBN (Electronic)978-1-7281-5446-6
ISBN (Print)978-1-7281-8409-8
DOIs
Publication statusPublished - 9 Apr 2021
Event2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020 - Biloxi, United States
Duration: 5 Oct 202030 Oct 2020

Publication series

NameGlobal Oceans 2020
PublisherIEEE
ISSN (Print)0197-7385

Conference

Conference2020 Global Oceans: Singapore - U.S. Gulf Coast, OCEANS 2020
Country/TerritoryUnited States
CityBiloxi
Period5/10/2030/10/20

Keywords / Materials (for Non-textual outputs)

  • Underwater manipulation
  • Reinforcement learning
  • Neural networks
  • Intelligent control
  • Deep Deterministic Policy Gradienr

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