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Learning in real robots from environment interaction

By Pablo Quintía Vidal, Roberto Iglesias Rodríguez, Miguel Ángel Rodríguez González, Carlos Vázquez Regueiro and Fernando Valdés Villarrubia


This article describes a proposal to achieve fast robot learning from its interaction with the environment. Our proposal will be suitable for continuous learning procedures as it tries to limit the instability that appears every time the robot encounters a new situation it had not seen before. On the other hand, the user will not have to establish a degree of exploration (usual in reinforcement learning) and that would prevent continual learning procedures. Our proposal will use an ensemble of learners able to combine dynamic programming and reinforcement learning to predict when a robot will make a mistake. This information will be used to dynamically evolve a set of control policies that determine the robot actions.This work was supported by the research grants TIN2009-07737 and INCITE08PXIB262202PR

Topics: Continuous robot learning, Robot adaptation, Learning from environment interaction, Reinforcement learning, Ciencia de la Computación e Inteligencia Artificial
Publisher: Red de Agentes Físicos
Year: 2012
OAI identifier:

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