Marco Piccinelli — accepted AI-conference papers

2022

3D Vision Based Robot Assisted Electrical Impedance Scanning for Soft Tissue Conductivity Sensing

Marco Piccinelli, Zhuoqi Cheng, Diego Dall'Alba, Michael Kjær Schmidt, Thiusius Rajeeth Savarimuthu, Paolo Fiorini

RA-L 2022

Advanced Sensing Technologies (ASTs) have a great potential to improve surgical quality and to further develop Surgical Robotic Systems (SRSs), enhancing their technical and autonomy capabilities. Among these sensing techniques, Electrical Bioimpedance (EBI) provides a non-invasive, low-cost, and sa

Cited by 2 Source ↗Scholar ↗

2021

Data-Driven Intra-Operative Estimation of Anatomical Attachments for Autonomous Tissue Dissection

Eleonora Tagliabue, Diego Dall'Alba, Micha Pfeiffer, Marco Piccinelli, Riccardo Marin, Umberto Castellani +2

RA-L 2021

The execution of surgical tasks by an Autonomous Robotic System (ARS) requires an up-to-date model of the current surgical environment, which has to be deduced from measurements collected during task execution. In this work, we propose to automate tissue dissection tasks by introducing a convolution

Cited by 18 Source ↗Scholar ↗

2020

Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery

Eleonora Tagliabue, Ameya Pore, Diego Dall’Alba, Enrico Magnabosco, Marco Piccinelli, Paolo Fiorini

IROS 2020poster

Reinforcement Learning (RL) methods have demonstrated promising results for the automation of subtasks in surgical robotic systems. Since many trial and error attempts are required to learn the optimal control policy, RL agent training can be performed in simulation and the learned behavior can be t…

Cited by 65 Source ↗Scholar ↗