The next stage for robotics development is to introduce autonomy and cooperation with human agents in tasks that require high levels of precision and/or that exert considerable physical strain. To guarantee the highest possible safety standards, the best approach is to devise a deterministic automaton that performs identically for each operation. Clearly, such approach inevitably fails to adapt itself to changing environments or different human companions. In a surgical scenario, the highest variability happens for the timing of different actions performed within the same phases. This paper presents a cognitive control architecture that uses a multi-modal neural network trained on a cooperative task performed by human surgeons and produces an action segmentation that provides the required timing for actions while maintaining full phase execution control via a deterministic Supervisory Controller and full execution safety by a velocity-constrained Model-Predictive Controller.


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    Title :

    A first evaluation of a multi-modal learning system to control surgical assistant robots via action segmentation


    Contributors:
    de Rossi G. (author) / Roin S. (author) / Falezza F. (author) / Setti F. (author) / Muradore R. (author) / Minelli M. (author) / Ferraguti F. (author) / Secchi C. (author) / Sozzi A. (author) / Bonfe' M. (author)

    Publication date :

    2021-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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