@article{Rossi2021,
title = {A First Evaluation of a Multi-Modal Learning System to Control Surgical Assistant Robots via Action Segmentation},
author = {Giacomo De Rossi, Marco Minelli, Serena Roin, Fabio Falezza, Alessio Sozzi, Federica Ferraguti, Francesco Setti, Marcello Bonfè, Cristian Secchi, Riccardo Muradore},
editor = {IEEE },
doi = {10.1109/TMRB.2021.3082210},
issn = {2576-3202},
year = {2021},
date = {2021-05-21},
journal = {IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS},
volume = {3},
number = {3},
pages = {714-724},
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.},
keywords = {Action segmentation, Cognitive robotics, Medical robotics, Model-predictive control, R-MIS},
pubstate = {published},
tppubtype = {article}
}

@article{Roberti2020,
title = {Improving Rigid 3-D Calibration for Robotic Surgery},
author = {Andrea Roberti , Nicola Piccinelli , Daniele Meli, Riccardo Muradore , and Paolo Fiorini ,},
editor = {IEEE },
doi = {10.1109/TMRB.2020.3033670},
isbn = {2576-3202},
year = {2020},
date = {2020-11-04},
journal = {IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS},
volume = {2},
number = {4},
pages = {569-573},
abstract = {Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this article, we propose a novel calibration technique for a surgical scenario with a da Vinci ® Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we significantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups. Moreover, our calibration method can be easily extended to standard surgical endoscope used in real surgical scenario.},
keywords = {Calibration, Medical robotics, Minimally invasive surgery, multi arm calibration, Robot, Robot vision systems, Surgery, Surgical robotics, Three-dimensional displays},
pubstate = {published},
tppubtype = {article}
}

@article{Oleari2019,
title = {Enhancing Surgical Process Modeling for Artificial Intelligence Development in Robotics the SARAS Case Study for Minimally Invasive Procedures},
author = {Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Federico Deho and Alessandro Larcher and Francesco Montorsi and Andrea Salonia and Francesco Setti and Riccardo Muradore},
editor = {IEEE},
doi = {10.1109/ISMICT.2019.8743931},
isbn = {978-1-7281-2342-4},
year = {2019},
date = {2019-05-09},
pages = {1-6},
abstract = {Nowadays Minimally Invasive Surgery (MIS) is playing an increasingly major role in the clinical practice also thanks to a rapid evolution of the available medical technologies, especially surgical robotics. A new challenge in this respect is to equip robots with cognitive capabilities, in order to make them able to act autonomously and cooperate with human surgeons. In this paper we describe the methodological approach developed to comprehensively describe a specific surgical knowledge, to be transferred to a complex Artificial Intelligence (AI) integrating Perception, Cognitive and Planning modules. Starting from desk researches and a strict cooperation with expert surgeons, the surgical process is framed on a high-level perspective, which is then deepened into a granular model through a Surgical Process Modelling approach, so as to embed all of the needed information by the AI to properly work. The model is eventually completed adding the corresponding Process Risk Analysis. We present the results obtained with the application of the aforementioned methodology to a Laparoscopic Radical Nephrectomy (LRN) procedure and discuss on the next technical implementation of this model.},
keywords = {Artificial Intelligence, Autonomy, Cognitive control, Cognitive functions, Decision making, Laparoscopes, Laparoscopy, Laparoscopy, learning systems, machine learning, Medical robotics, multirobots teleoperated platform, Robotic surgery, Surgery, Surgical robots, Teleoperation},
pubstate = {published},
tppubtype = {article}
}