@conference{Rossi2020,
title = {Cognitive Robotic Architecture for Semi-Autonomous Execution of Manipulation Tasks in a Surgical Environment},
author = {Giacomo De Rossi, Marco Minelli, Alessio Sozzi, Nicola Piccinelli, Federica Ferraguti, Francesco Setti, Marcello Bonfé, Christian Secchi, Riccardo Muradore},
editor = {IEEE International Intelligent Robots and Systems (IROS)},
doi = {10.1109/IROS40897.2019.8967667},
isbn = {978-1-7281-4004-9},
year = {2020},
date = {2020-01-27},
booktitle = {2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
publisher = {IEEE},
abstract = {The development of robotic systems with a certain level of autonomy to be used in critical scenarios, such as an operating room, necessarily requires a seamless integration of multiple state-of-the-art technologies. In this paper we propose a cognitive robotic architecture that is able to help an operator accomplish a specific task. The architecture integrates an action recognition module to understand the scene, a supervisory control to make decisions, and a model predictive control to plan collision-free trajectory for the robotic arm taking into account obstacles and model uncertainty. The proposed approach has been validated on a simplified scenario involving only a da VinciO surgical robot and a novel manipulator holding standard laparoscopic tools.},
keywords = {Artificial Intelligence, Collision avoidance, Manipulators, Medical robotics, mobile robots, predictive control, Robot, Robot vision, Surgery, trajectory control, uncertain systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Casals2019,
title = {Assistance Strategies for Robotized Laparoscopy},
author = {Alicia Casals and Albert Hernansanz and Narcís Sayols and Josep Amat},
editor = {Springer
},
url = {https://link.springer.com/chapter/10.1007%2F978-3-030-36150-1_40},
doi = {10.1007/978-3-030-36150-1_40},
isbn = {978-3-030-36149-5},
year = {2019},
date = {2019-11-20},
booktitle = {Robot 2019: Fourth Iberian Robotics Conference},
pages = {485-496},
abstract = {Robotizing laparoscopic surgery not only allows achieving better accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.},
keywords = {Cooperative robotics, Laparoscopy, Robot, Safety, Surgery, Surgical robots, Virtual feedback},
pubstate = {published},
tppubtype = {conference}
}

@conference{Minelli2019,
title = {Energy-Shared Two-Layer (Approach for Multi-Master-Multi-Slave) Bilateral Teleoperation Systems},
author = {Marco Minelli and Federica Ferraguti and Nicola Piccinelli and Riccardo Muradore and Cristian Secchi},
url = {https://nxgsur-icra2019.sciencesconf.org/272549/document},
doi = {10.5281/zenodo.3362947 },
year = {2019},
date = {2019-05-20},
abstract = {In this paper, a two-layer architecture for the bilateral teleoperation of multi-arms systems with communication delay is presented. We extend the single-master-single-slave two layer approach proposed in [1] by connecting multiple robots to a single energy tank. This allows to minimize the conservativeness due to passivity preservation and to increment the level of transparency that can be achieved. The proposed approach is implemented on a realistic surgical scenario developed within the EU-funded SARAS project.
},
keywords = {Control architecture, Control programming, Laparoscopy, Robot, Surgical robots, Teleoperation, Telerobotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Setti2019,
title = {A Multirobots Teleoperated Platform for Artificial Intelligence Training Data Collection in Minimally Invasive Surgery},
author = {Francesco Setti and Elettra Oleari and Alice Leporini and Diana Trojaniello and Alberto Sanna and Umberto Capitanio and Francesco Montorsi and Andrea Salonia and Riccardo Muradore},
editor = {IEEE},
url = {http://bmvc2018.org/contents/papers/0593.pdf},
doi = {10.1109/ISMR.2019.8710209},
isbn = {978-1-5386-7825-1},
year = {2019},
date = {2019-05-09},
pages = {1-7},
abstract = {Dexterity and perception capabilities of surgical robots may soon be improved by cognitive functions that can support surgeons in decision making and performance monitoring, and enhance the impact of automation within the operating rooms. Nowadays, the basic elements of autonomy in robotic surgery are still not well understood and their mutual interaction is unexplored. Current classification of autonomy encompasses six basic levels: Level 0: no autonomy; Level 1: robot assistance; Level 2: task autonomy; Level 3: conditional autonomy; Level 4: high autonomy. Level 5: full autonomy. The practical meaning of each level and the necessary technologies to move from one level to the next are the subject of intense debate and development. In this paper, we discuss the first outcomes of the European funded project Smart Autonomous Robotic Assistant Surgeon (SARAS). SARAS will develop a cognitive architecture able to make decisions based on pre-operative knowledge and on scene understanding via advanced machine learning algorithms. To reach this ambitious goal that allows us to reach Level 1 and 2, it is of paramount importance to collect reliable data to train the algorithms. We will present the experimental setup to collect the data for a complex surgical procedure (Robotic Assisted Radical Prostatectomy) on very sophisticated manikins (i.e. phantoms of the inflated human abdomen). The SARAS platform allows the main surgeon and the assistant to teleoperate two independent two-arm robots. The data acquired with this platform (videos, kinematics, audio) will be used in our project and will be released (with annotations) for research purposes.},
keywords = {Artificial Intelligence, Cognitive control, Computer Science, Laparoscopy, Laparoscopy, machine learning, Robot, Robotic surgery, Surgery, Teleoperation},
pubstate = {published},
tppubtype = {conference}
}

@conference{Hernansanz2019,
title = {A physical/virtual platform for hysteroscopy training},
author = {Albert Hernansanz and Martínez and Rovira and Alicia Casals},
editor = {CRAS 2019},
doi = {10.5281/zenodo.3373297},
year = {2019},
date = {2019-03-21},
booktitle = {Proceedings of the 9th Joint Workshop on New Technologies for Computer/Robot Assisted Surgery},
abstract = {This work presents HysTrainer (HT), a training module for hysteroscopy, which is part of the generic endoscopic training platform EndoTrainer (ET). This platform merges both technologies, with the benefits of having a physical anatomic model and computer assistance for augmented reality and objective assessment. Further to the functions of a surgical trainer, EndoTrainer provides an integral education, training and evaluation platform.},
keywords = {Computer Science, Endoscopy, Laparoscopy, Laparoscopy, Robot, Robotic surgery, Robotic Surgery, Surgery, Surgical robots, Training},
pubstate = {published},
tppubtype = {conference}
}

@conference{González2018,
title = {Estimation of interaction forces in robotic surgery using a semi-supervised deep neural network model},
author = {Marbán Arturo and Vignesh Srinivasan and Wojciech Samek and Josep Fernández and Alicia Casals},
editor = {IEEE},
url = {https://upcommons.upc.edu/bitstream/handle/2117/132610/iros2018_paper_26_07_2018.pdf?sequence=3&isAllowed=y},
doi = {10.1109/IROS.2018.8593701},
year = {2018},
date = {2018-08-09},
abstract = {Providing force feedback as a feature in current Robot-Assisted Minimally Invasive Surgery systems still remains a challenge. In recent years, Vision-Based Force Sensing (VBFS) has emerged as a promising approach to address this problem. Existing methods have been developed in a Supervised Learning (SL) setting. Nonetheless, most of the video sequences related to robotic surgery are not provided with ground-truth force data, which can be easily acquired in a controlled environment. A powerful approach to process unlabeled video sequences and find a compact representation for each video frame relies on using an Unsupervised Learning (UL) method. Afterward, a model trained in an SL setting can take advantage of the available ground-truth force data. In the present work, UL and SL techniques are used to investigate a model in a Semi-Supervised Learning (SSL) framework, consisting of an encoder network and a Long-Short Term Memory (LSTM) network. First, a Convolutional Auto-Encoder (CAE) is trained to learn a compact representation for each RGB frame in a video sequence. To facilitate the reconstruction of high and low frequencies found in images, this CAE is optimized using an adversarial framework and a L1-loss, respectively. Thereafter, the encoder network of the CAE is serially connected with an LSTM network and trained jointly to minimize the difference between ground-truth and estimated force data. Datasets addressing the force estimation task are scarce. Therefore, the experiments have been validated in a custom dataset. The results suggest that the proposed approach is promising.},
keywords = {Learning, Robot, Robotic surgery, Robotics, Surgery, Training},
pubstate = {published},
tppubtype = {conference}
}