73 citations · 193 across the 12 of their papers we have counts for
6 papers · 1 filter
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…