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

Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods
that exhibit the desired motion quality for delicate surgical interventions. To this end, we …

Recent applications of deep reinforcement learning (DRL) in surgical autonomy have shown
promising results in automating various surgical sub-tasks. While most of these studies …

Accurate deformable object manipulation (DOM) is essential for achieving autonomy in
robotic surgery, where soft tissues are being displaced, stretched, and dissected. Many DOM …

Off-policy reinforcement learning (RL) algorithms are known for improving sample efficiency
by employing prior experiences in experience replay memory. However, most existing off …

There is a large number of robotic platforms with two or more arms targeting surgical
applications. Despite that, very few groups have employed such platforms for scientific …

Recent studies in surgical robotics have focused on automating common surgical subtasks
such as grasping and manipulation using deep reinforcement learning (DRL). In this work …

Surgical robot simulation platform plays a crucial role in enhancing training efficiency and
advancing research on robot learning. Much effort have been made by scholars on …

Despite advancements in robotic-assisted surgery, automating complex tasks like suturing
remain challenging due to the need for adaptability and precision. Learning-based …

The rise of Large Language Models (LLMs) has impacted research in robotics and
automation. While progress has been made in integrating LLMs into general robotics tasks …