@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{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{Roberti2018b,
title = {An energy saving approach to active object recognition and localization},
author = {Andrea Roberti and Riccardo Muradore and Paolo Fiorini and Marco Cristani and Francesco Setti},
editor = {IECON 2018},
doi = {10.1109/IECON.2018.8591411},
year = {2018},
date = {2018-10-21},
organization = {Annual Conference of the IEEE Industrial Electronics Society (IECON).Washington, DC, USA. },
abstract = {We propose an active object recognition (AOR) strategy explicitly suited to work with a real robotic arm. So far, AOR policies on robotic arms have focused on heterogeneous constraints, most of them related to classification accuracy, classification confidence, number of moves etc., discarding physical and energetic constraints a real robot has to fulfill. Our strategy adjusts this discrepancy, with a POMDP-based AOR algorithm that explicitly considers manipulability and energetic terms in the planning optimization. The manipulability term avoids the robotic arm to encounter singularities, which require expensive and straining backtracking steps; the energetic term deals with the arm gravity compensation when in static conditions, which is crucial in AOR policies where time is spent in the classifier belief update, before to do the next move. Several experiments have been carried out on a redundant, 7-DoF Panda arm manipulator, on a multi-object recognition task. This allows to appreciate the improvement of our solution with respect to other competitors evaluated on simulations only.},
keywords = {Active object recognition, Artificial Intelligence, Computer Science, Learning, Object recognition, Pattern Recognition, POMDP, Robotics},
pubstate = {published},
tppubtype = {conference}
}

@conference{Roberti2018,
title = {Recognition self-awareness for active object recognition on depth images},
author = {Andrea Roberti and Marco Carletti and Francesco Setti and Umberto Castellani and Paolo Fiorini and Marco Cristani},
editor = {British Machine Vision Conference (BMVC). Newcastle-Upon-Tyne, UK. (bmvc2018.org). Spotlight, 2% acceptance rate.},
url = {http://bmvc2018.org/contents/papers/0593.pdf},
doi = {10.5281/zenodo.3362923},
year = {2018},
date = {2018-09-06},
organization = {BMVC 2018},
abstract = {We propose an active object recognition framework that introduces the recognition self-awareness, which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built first by learning a multi-view deep 3D object classifier; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classifier considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP). In practice, the robot decides which views to cover, depending on the expected ability of the classifier to discriminate an object class by observing a specific part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classifier has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives.},
keywords = {3D object classifier, Artificial Intelligence, Computer Science, Object exploration, Object recognition, POMDP},
pubstate = {published},
tppubtype = {conference}
}