A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation

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A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation

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Giacomo De Rossi / Marco Minelli / Serena Roin / Fabio Falezza / Alessio Sozzi / Federica Ferraguti / Francesco Setti / Marcello Bonfe / Cristian Secchi / Riccardo Muradore

Abstract

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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Document information

Title :

A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation

Contributors:

Giacomo De Rossi (author) / Marco Minelli (author) / Serena Roin (author) / Fabio Falezza (author) / Alessio Sozzi (author) / Federica Ferraguti (author) / Francesco Setti (author) / Marcello Bonfe (author) / Cristian Secchi (author) / Riccardo Muradore (author)

Publication date :

2021-01-01

DOI :

https://doi.org/10.1109/tmrb.2021.3082210

Type of media :

Article (Journal)

Type of material :

Electronic Resource

Language :

English

Keywords :

surgical robotics , statecharts , supervisory controller , autonomous robotics