5 accepted papers
Reinforcement Learning (RL) algorithms can learn robotic control tasks from visual observations, but they often require a large amount of data, especially when the visual scene is complex and unstructured. In this paper, we explore how the agent’s knowledge of its shape can improve the sample effici…
Robotic surgery is a rapidly developing field that can greatly benefit from the automation of surgical tasks. However, training techniques such as Reinforcement Learning (RL) require a high number of task repetitions, which are generally unsafe and impractical to perform on real surgical systems. Th…
In this paper, we propose a novel bilateral teleoperation architecture that allows to optimally render the remote interaction force at the local side while guaranteeing a robustly stable behaviour. Stability is guaranteed by ensuring a proper energy exchange between the local and the remote sides. D…
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 task automation could lead to reduced surgeon’s cognitive workload, increased precision in critical aspects of the surgery,…
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…