It is important to investigate object perception for classification or recognition based on touch sensing, especially when robots are operating in darkness or the objects are difficult to capture by vision sensors. In this work, we present a new form of continuum manipulator equipped with sparse touch sensing, validate the effectiveness of automatic generation of the touch-based continuum wraps, and the effectiveness of object classification based on the continuum wraps. Using the indirect object shape information encoded in the robot shape, we demonstrate that a classifier trained from the simulated continuum wraps is transferable to identify the real world objects with real continuum wraps.


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    Title :

    Sim-to-Real Transferable Object Classification Through Touch-Based Continuum Manipulation


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:
    Xiao, Jing (editor) / Kröger, Torsten (editor) / Khatib, Oussama (editor) / Mao, Huitan (author) / Santoso, Junius (author) / Onal, Cagdas (author) / Xiao, Jing (author)

    Conference:

    International Symposium on Experimental Robotics ; 2018 ; Buenos Aires, Argentina November 05, 2018 - November 08, 2018



    Publication date :

    2020-01-23


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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