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

BARBALATA, CORINA; CARLUCHO, IGNACIO; ACOSTA, GERARDO G.; DE PAULA, MARIANO

Mississippi Gulf Coast

Congreso; OCEANS 2020; 2020

MTS/IEEE

In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, lowlevel control methods require great capabilities to adapt to change. Furthermore, under position and torque constraintsthe 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.