Research and application of reinforcement learning in robotics for contact-rich manipulation
tasks have exploded in recent years. Its ability to cope with unstructured environments and …

The scale of Internet-connected systems has increased considerably, and these systems are
being exposed to cyberattacks more than ever. The complexity and dynamics of …

Medical robots have demonstrated the ability to manipulate percutaneous instruments into
soft tissue anatomy while working beyond the limits of human perception and dexterity …

Task automation of surgical robot has the potentials to improve surgical efficiency. Recent
reinforcement learning (RL) based approaches provide scalable solutions to surgical …

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 …

Robot-assisted microsurgery (RAMS) has many benefits compared to traditional
microsurgery. Microsurgical platforms with advanced control strategies, high-quality micro …

Reinforcement Learning (RL) methods have demonstrated promising results for the
automation of subtasks in surgical robotic systems. Since many trial and error attempts are …

Recent advances in robot-assisted surgery have resulted in progressively more precise,
efficient, and minimally invasive procedures, sparking a new era of robotic surgical …

In the last five years, deep learning has attracted great interest in computer-assisted systems
for Minimally Invasive Surgery. The straightforward accessibility to images in surgical …

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 …