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Architecture cognitive générique pour la coordination de stratégies d'apprentissage en robotique

Abstract : The main objective of this thesis is to propose a new method for online adaptation of robotic learning, allowing robots to dynamically and autonomously adapt their behavior according to variations in their own performance. The developed method is sufficiently general and task-independent that a robot using it can perform different dynamic tasks of various nature without any algorithm or parameter adjustment by the programmer. The algorithms underlying this method consist of a meta-control system that allows the robot to call upon two decision-making experts following a different behavioral strategy. The model-based expert builds a model of the effects of long-term actions and uses this model to decide; this strategy is computationally expensive, but quickly converges to the solution. The model-free expert is inexpensive in terms of computational resources, but takes time to converge to the optimal solution. In this work, we have developed a new criterion for the coordination of these two experts allowing the robot to dynamically change its strategy over time. We show in this work that our behavior coordination method allows the robot to maintain an optimal performance in terms of performance and computation time. We also show that the method can cope with abrupt changes in the environment, changes in goals or changes in the behavior of the human partner in the case of interaction tasks.
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Submitted on : Monday, May 30, 2022 - 2:56:05 PM
Last modification on : Wednesday, June 1, 2022 - 1:03:29 PM


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  • HAL Id : tel-03681701, version 1


Rémi Dromnelle. Architecture cognitive générique pour la coordination de stratégies d'apprentissage en robotique. Réseau de neurones [cs.NE]. Sorbonne Université, 2021. Français. ⟨NNT : 2021SORUS039⟩. ⟨tel-03681701⟩



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