A Robot that Autonomously Improves Skills by Evolving Computational Graphs

Lorenzo Riano, TM McGinnity

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Abstract

    We propose an evolutionary algorithm to au- tonomously improve the performances of a robotics skill. The algorithm extends a previously proposed graphical evolutionary skills building approach to allow a robot to autonomously collect use cases where a skill fails and use them to improve the skill. Here we define a computational graph as a generic model to hierarchically represent skills and to modify them. The computational graph makes use of embedded neural networks to create generic skills. We tested our proposed algorithm on a real robot implementing a “move to reach” action. Four experiments show the evolution of the computational graph as it is adapted to solve increasingly complex problems.
    Original languageEnglish
    Title of host publicationUnknown Host Publication
    PublisherIEEE
    Number of pages8
    Publication statusPublished - 10 Jun 2012
    Event2012 IEEE Congress on Evolutionary Computation - Brisbane, Australia
    Duration: 10 Jun 2012 → …

    Conference

    Conference2012 IEEE Congress on Evolutionary Computation
    Period10/06/12 → …

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