Reinforcement learning has become more and more popular in robotics for acquiring feedback controllers. Many approaches aim for learning a controller from scratch, i.e., data-driven without any modeling of the physical plant. However, stability properties of the closed loop are often not considered, or established only a-posteriori or ad hoc. We propose to employ reinforcement learning in the context of model-based control, allowing to learn in a framework of stabilizing controllers built by using only little prior model knowledge. This way, the action space is suitably structured for safe learning of a feedback controller to compensate for uncertainties due to model mismatch or external disturbances. The resulting scheme is developed around a decentralized PD feedback controller. Therefore, given such a controller, by the proposed method one can also add a learning module for performance enhancement. We demonstrate our approach both in simulation and in a hardware experiment using a two degree of freedom robot manipulator.


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    Title :

    A robust stability approach to robot reinforcement learning based on a parameterization of stabilizing controllers


    Contributors:
    Friedrich, S. R. (author) / Buss, M. (author)

    Publication date :

    2018-01-31



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629 / 620




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