In this chapter, we present a novel method for incremental learning robot complex task representation, identifying repeated skills, and generalizing to new environment by heuristically segmenting the unstructured demonstrations into movement primitives that modelled with a dynamical system. The proposed method combines the advantages of recent task representation methods for learning from demonstration in into an integrated framework. In particular, we use the combination of finite state machine and dynamical movement primitives for complex task representation, and investigate the Bayesian nonparametric hidden Markov model for repeated skill identification. To this end, a robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of skills, which helps a robot understand what it is doing at all times. Two complex, multi-step robot tasks are designed to evaluate the feasibility and effectiveness of proposed methods. We not only present the results in task representation, but also analyzing the performance of skill identification by various nonparametric models with various modality combinations.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Incremental Learning Robot Task Representation and Identification


    Beteiligte:
    Zhou, Xuefeng (Autor:in) / Wu, Hongmin (Autor:in) / Rojas, Juan (Autor:in) / Xu, Zhihao (Autor:in) / Li, Shuai (Autor:in)


    Erscheinungsdatum :

    22.07.2020


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Incremental Learning of Task Sequences with Information-Theoretic Metrics

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


    Incremental Simulation for Part Task Trainers

    Rosser, B. / Graham, D. / Royal Aeronautical Society; Flight Simulation Group | British Library Conference Proceedings | 1993


    Incremental learning of skills in a task-parameterized Gaussian Mixture Model

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

    Freier Zugriff

    Task Representation

    Vakanski, Aleksandar / Janabi‐Sharifi, Farrokh | Wiley | 2017