Pubblicazioni

Autori:

Tagliabue, Eleonora; Pore, AMEYA RAVINDRA; Dall'Alba, Diego; Magnabosco, Enrico; Piccinelli, Marco; Fiorini, Paolo

Titolo:

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

Anno:

2020

Tipologia prodotto:

Contributo in atti di convegno

Tipologia ANVUR:

Contributo in Atti di convegno

Lingua:

Inglese

Titolo del Convegno:

IEEE/RSJ International Conference on Intelligent Robots and Systems (2020)

Luogo:

Las Vegas (US)

Periodo:

2020 Oct 25-29

Intervallo pagine:

3261-3266

Parole chiave:

soft tissue simulation; reinforcement learning; autonomous tissue manipulation

Breve descrizione dei contenuti:

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 then deployed in real environments. In this work, we introduce an open-source simulation environment providing support for position based dynamics soft bodies simulation and state-of-the-art RL methods. We demonstrate the capabilities of the proposed framework by training an RL agent based on Proximal Policy Optimization in fat tissue manipulation for tumor exposure during a nephrectomy procedure. Leveraging on a preliminary optimization of the simulation parameters, we show that our agent is able to learn the task on a virtual replica of the anatomical environment. The learned behavior is robust to changes in the initial end-effector position. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the RL agent. The proposed simulation environment represents an essential component for the development of next-generation robotic systems, where the interaction with the deformable anatomical environment is involved.

Id prodotto:

117247

Handle IRIS:

11562/1027625

ultima modifica:

17 aprile 2025

Citazione bibliografica:

Tagliabue, Eleonora; Pore, AMEYA RAVINDRA; Dall'Alba, Diego; Magnabosco, Enrico; Piccinelli, Marco; Fiorini, Paolo, Soft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery  in IROS 2020 Proceedings , Atti di "IEEE/RSJ International Conference on Intelligent Robots and Systems (2020)" , Las Vegas (US) , 2020 Oct 25-29 , 2020 , pp. 3261-3266

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