82 citations · 457 across the 22 of their papers we have counts for
16 papers · 1 filter
Sim-To-Real Transfer for Visual Reinforcement Learning of Deformable Object Manipulation for Robot-Assisted Surgery
Paul Maria Scheikl, Eleonora Tagliabue, Balázs Gyenes +4
Automation holds the potential to assist surgeons in robotic interventions, shifting their mental work load from visuomotor control to high level decision making. Reinforcement lea…
Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review
Ameya Pore, Zhen Li, Diego Dall'Alba +7
Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and ves…
Constrained Reinforcement Learning and Formal Verification for Safe Colonoscopy Navigation
Davide Corsi, Luca Marzari, Ameya Pore +4
The field of robotic Flexible Endoscopes (FEs) has progressed significantly, offering a promising solution to reduce patient discomfort. However, the limited autonomy of most robot…
Generalization of Auto-Regressive Hidden Markov Models to Non-Linear Dynamics and Unit Quaternion Observation Space
Michele Ginesi, Paolo Fiorini
Latent variable models are widely used to perform unsupervised segmentation of time series in different context such as robotics, speech recognition, and economics. One of the most…
Logic programming for deliberative robotic task planning
Daniele Meli, Hirenkumar Nakawala, Paolo Fiorini
Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robo…
Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User Study
Ameya Pore, Martina Finocchiaro, Diego Dall'Alba +6
Flexible endoscopes for colonoscopy present several limitations due to their inherent complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic…
Deliberation in autonomous robotic surgery: a framework for handling anatomical uncertainty
Eleonora Tagliabue, Daniele Meli, Diego Dall'Alba +1
Autonomous robotic surgery requires deliberation, i.e. the ability to plan and execute a task adapting to uncertain and dynamic environments. Uncertainty in the surgical domain is…
Learning from Demonstrations for Autonomous Soft-tissue Retraction
Ameya Pore, Eleonora Tagliabue, Marco Piccinelli +3
The current research focus in Robot-Assisted Minimally Invasive Surgery (RAMIS) is directed towards increasing the level of robot autonomy, to place surgeons in a supervisory posit…
Autonomous tissue retraction with a biomechanically informed logic based framework
D. Meli, E. Tagliabue, D. Dall'Alba +1
Autonomy in robot-assisted surgery is essential to reduce surgeons' cognitive load and eventually improve the overall surgical outcome. A key requirement for autonomy in a safety-c…
Safe Reinforcement Learning using Formal Verification for Tissue Retraction in Autonomous Robotic-Assisted Surgery
Ameya Pore, Davide Corsi, Enrico Marchesini +4
Deep Reinforcement Learning (DRL) is a viable solution for automating repetitive surgical subtasks due to its ability to learn complex behaviours in a dynamic environment. This tas…
Industry 4.0 and Prospects of Circular Economy: A Survey of Robotic Assembly and Disassembly
Morteza Daneshmand, Fatemeh Noroozi, Ciprian Corneanu +2
Despite their contributions to the financial efficiency and environmental sustainability of industrial processes, robotic assembly and disassembly have been understudied in the exi…
Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks
Luca Marzari, Ameya Pore, Diego Dall'Alba +3
Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the data-hungr…
Improving rigid 3D calibration for robotic surgery
Andrea Roberti, Nicola Piccinelli, Daniele Meli +2
Autonomy is the 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…
Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions
Michele Ginesi, Daniele Meli, Andrea Roberti +2
Obstacle avoidance for DMPs is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to repres…
Autonomous task planning and situation awareness in robotic surgery
Michele Ginesi, Daniele Meli, Andrea Roberti +2
The use of robots in minimally invasive surgery has improved the quality of standard surgical procedures. So far, only the automation of simple surgical actions has been investigat…
Overcoming Some Drawbacks of Dynamic Movement Primitives
Michele Ginesi, Nicola Sansonetto, Paolo Fiorini
Dynamic Movement Primitives (DMPs) is a framework for learning a point-to-point trajectory from a demonstration. Despite being widely used, DMPs still present some shortcomings tha…