Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in robot design and recent applications for exploiting data with specific geometries, it mostly remains overlooked in robotics. With this blue sky paper, we argue that Riemannian geometry provides the most suitable tools to analyze and generate well-coordinated, energy-efficient motions of robots with many degrees of freedom. Via preliminary solutions and novel research directions, we discuss how Riemannian geometry may be leveraged to design and combine physically-meaningful synergies for robotics, and how this theory also opens the door to coupling motion synergies with perceptual inputs.


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    Titel :

    Riemannian Geometry as a Unifying Theory for Robot Motion Learning and Control


    Weitere Titelangaben:

    Springer Proceedings in Advanced Robotics


    Beteiligte:
    Billard, Aude (Herausgeber:in) / Asfour, Tamim (Herausgeber:in) / Khatib, Oussama (Herausgeber:in) / Jaquier, Noémie (Autor:in) / Asfour, Tamim (Autor:in)

    Kongress:

    The International Symposium of Robotics Research ; 2022 ; Geneva, Switzerland September 25, 2022 - September 30, 2022


    Erschienen in:

    Robotics Research ; Kapitel : 27 ; 395-403


    Erscheinungsdatum :

    08.03.2023


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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