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.
A first evaluation of a multi-modal learning system to control surgical assistant robots via action segmentation
2021-01-01
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Human Assistant Planetary Exploration Robots
British Library Conference Proceedings | 2006
|Human Assistant Planetary Exploration Robots
ASCE | 2006
|Human Assistant Planetary Exploration Robots
British Library Conference Proceedings | 2006
|