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A hybrid reinforcement learning perspective for autonomous mobile robot control

Ignacio Carlucho; Mariano De Paula; Gerardo G. Acosta;

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The low-level control of autonomous mobile robots has been extensivelyaddressed by classical control techniques. However, the variable operativeconditions and different environmental factors faced by these robots havedriven researchers towards the formulation of adaptive control approaches.In this sense, artificial intelligence techniques seem promising since theycan provide a higher level of abstraction to the robot, allowing for moregeneral decision making. Particularly, within these techniques, the rein-forcement learning paradigm has excelled in solving the most diverse typeof problems, by providing a model free unsupervised solution. Further-more, recent developments in the deep reinforcement learning field haveallowed the use of deep neural networks as function approximators of thepolicy function, increasing the generalization performance. In this thesisthe author studies the capabilities of the reinforcement learning paradigmfor the real time low-level control of mobile robots. Making use of existingclassical control techniques and reinforcement learning, hybrid controllersare obtained that take the best of both worlds, enhancing the overall per-formance and effectively achieving adaptive controllers. Extensive resultsin simulation and on different robotic platforms show the promising ap-plicability of these intelligent adaptive controllers for autonomous robots.
Robots móviles autónomos; Refuerzos híbridos; Estrategias inteligentes de control; Robótica; Inteligencia artificial; Redes neuronales;

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