A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation
| Main Authors: | Giacomo De Rossi, Marco Minelli, Serena Roin, Fabio Falezza, Alessio Sozzi, Federica Ferraguti, Francesco Setti, Marcello Bonfè, Cristian Secchi, Riccardo Muradore |
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| Format: | Article Journal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5642704 |
Daftar Isi:
- 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.