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    Chapter Viscoelasticity in Foot-Ground Interaction

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    Author(s)
    Naemi, Roozbeh
    Behforootan, Sara
    Chatzistergos, Panagiotis
    Chockalingam, Nachiappan
    Language
    English
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    Abstract
    Dynamical models of robots performing tasks in contact with objects or the environment are difficult to obtain. Therefore, different methods of learning the dynamics of tasks have been proposed. In this chapter, we present a method that provides the joint torques needed to execute a task in a compliant and at the same time accurate manner. The presented method of compliant movement primitives (CMPs), which consists of the task kinematical and dynamical trajectories, goes beyond mere reproduction of previously learned motions. Using statistical generalization, the method allows to generate new, previously untrained trajectories. Furthermore, the use of transition graphs allows us to combine parts of previously learned motions and thus generate new ones. In the chapter, we provide a brief overview of this research topic in the literature, followed by an in-depth explanation of the compliant movement primitives framework, with details on both statistical generalization and transition graphs. An extensive experimental evaluation demonstrates the applicability and the usefulness of the approach.
    URI
    https://library.oapen.org/handle/20.500.12657/49150
    Keywords
    compliant movements, adaptive system, learning system, robot control, learning by demonstration
    DOI
    10.5772/64170
    Publisher
    InTechOpen
    Publisher website
    https://www.intechopen.com/
    Publication date and place
    2016
    Classification
    Computer vision
    Rights
    https://creativecommons.org/licenses/by/3.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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