73 citations · 193 across the 12 of their papers we have counts for
13 papers
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…
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez +6
Automatic recognition of fine-grained surgical activities, called steps, is a challenging but crucial task for intelligent intra-operative computer assistance. The development of c…
CIPCaD-Bench: Continuous Industrial Process datasets for benchmarking Causal Discovery methods
Giovanni Menegozzo, Diego Dall'Alba, Paolo Fiorini
Causal relationships are commonly examined in manufacturing processes to support faults investigations, perform interventions, and make strategic decisions. Industry 4.0 has made a…
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…
Data Stream Stabilization for Optical Coherence Tomography Volumetric Scanning
Guiqiu Liao, Oscar Caravaca-Mora, Benoit Rosa +7
Optical Coherence Tomography (OCT) is an emerging medical imaging modality for luminal organ diagnosis. The non-constant rotation speed of optical components in the OCT catheter ti…
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…
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Sanat Ramesh, Diego Dall'Alba, Cristians Gonzalez +6
Purpose: Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities…
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…