Parallel robot learning through an ensemble of predictors able to forecast the time interval before a robot failure
This article describes a proposal to achieve fast robot learning from its interaction with the environment. Our proposal will use an ensemble of elements that will combine dynamic programming and reinforcement to predict when a robot will make a mistake. This information will be used to change the control policy trying to maximize the time interval before a failure. Finally, our proposal will be able to learn simultaneously perception and action. The robot will build a representation of the environment that will dynamically increase, during the learning process, to include new situations that have not been seen before.
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Publication: Congress
1624015006665
June 18, 2021
/research/publications/parallel-robot-learning-through-an-ensemble-of-predictors-able-to-forecast-the-time-interval-before-a-robot-failure
This article describes a proposal to achieve fast robot learning from its interaction with the environment. Our proposal will use an ensemble of elements that will combine dynamic programming and reinforcement to predict when a robot will make a mistake. This information will be used to change the control policy trying to maximize the time interval before a failure. Finally, our proposal will be able to learn simultaneously perception and action. The robot will build a representation of the environment that will dynamically increase, during the learning process, to include new situations that have not been seen before. - M. A. Rodríguez, R. Iglesias, P. Quintía, C. V. Regueiro
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