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arXiv · 2011.12360

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

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

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Ignacio Carlucho, Mariano De Paula, Gerardo G. Acosta, Corina Barbalata. 2020-11-24. A reinforcement learning control approach for underwater manipulation under position and torque constraints. https://doi.org/10.1109/ieeeconf38699.2020.9389378

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