A reinforcement learning control approach for underwater manipulation under position and torque constraints
Abstract
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.
Cite
@article{arxiv.2011.12360,
title = {A reinforcement learning control approach for underwater manipulation under position and torque constraints},
author = {Ignacio Carlucho and Mariano De Paula and Gerardo G. Acosta and Corina Barbalata},
journal= {arXiv preprint arXiv:2011.12360},
year = {2021}
}