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