The final publication is available at link.springer.com ; Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones used to estimate the model need to be added. One of the ways to re-train a robot is by incremental learning, which supplies additional information of the task and does not require teaching the whole task again. The present study proposes three techniques to add trajectories to a previously estimated task-parameterized Gaussian mixture model. The first technique estimates a new model by accumulating the new trajectory and the set of trajectories generated using the previous model. The second technique permits adding to the parameters of the existent model those obtained for the new trajectories. The third one updates the model parameters by running a modified version of the Expectation-Maximization algorithm, with the information of the new trajectories. The techniques were evaluated in a simulated task and a real one, and they showed better performance than that of the existent model. ; Peer Reviewed ; Postprint (author's final draft)


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Incremental learning of skills in a task-parameterized Gaussian mixture model

    Hoyos, Jose / Prieto, Flavio / Alenyà, Guillem et al. | BASE | 2016

    Freier Zugriff

    Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and Priors

    Wang, Letian / Liu, Jie / Shao, Hao et al. | ArXiv | 2023

    Freier Zugriff

    Incremental Learning Robot Task Representation and Identification

    Zhou, Xuefeng / Wu, Hongmin / Rojas, Juan et al. | Springer Verlag | 2020

    Freier Zugriff

    Incremental Learning of Task Sequences with Information-Theoretic Metrics

    Pardowitz, Michael / Zöllner, Raoul / Dillmann, Rudiger | Springer Verlag | 2006


    A self-adaptive Gaussian mixture model

    Chen, Z. / Ellis, T. | British Library Online Contents | 2014