Movement primitives (MPs) have been widely adopted for representing and learning robotic movements using reinforcement learning policy search. Probabilistic movement primitives (ProMPs) are a kind of MP based on a stochastic representation over sets of trajectories, able to capture the variability allowed while executing a movement. This approach has proved effective in learning a wide range of robotic movements, but it comes with the necessity of dealing with a high-dimensional space of parameters. This may be a critical problem when learning tasks with two robotic manipulators, and this work proposes an approach to reduce the dimension of the parameter space based on the exploitation of symmetry. A symmetrization method for ProMPs is presented and used to represent two movements, employing a single ProMP for the first arm and a symmetry surface that maps that ProMP to the second arm. This symmetric representation is then adopted in reinforcement learning of bimanual tasks (from user-provided demonstrations), using relative entropy policy search algorithm. The symmetry-based approach developed has been tested in an experiment of cloth manipulation, showing a speed increment in learning the task.


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    Title :

    Exploiting symmetries in reinforcement learning of bimanual robotic tasks


    Contributors:

    Publication date :

    2019-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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