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 …

Robot-assisted urologic surgery coupled with machine learning is a burgeoning area of
study that demonstrates exciting potential. However, further validation and clinical trials are …

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 …

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

Suturing is required in almost all surgeries but it is challenging to perform with surgical
robots due to limited vision and/or haptic feedback. To tackle this problem, we present an …

Autonomy in robotic surgery is very challenging in unstructured environments, especially
when interacting with deformable soft tissues. The main difficulty is to generate model-based …

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 advances in depth-sensing have significantly increased accuracy, resolution, and
frame rate, as shown in the 1920x1200 resolution and 13 frames per second Zivid RGBD …

Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a
high-tech discipline. Increasingly powerful technological developments such as modern …

Autonomous robotic surgery requires deliberation, ie the ability to plan and execute a task
adapting to uncer-tain and dynamic environments. Uncertainty in the surgical domain is …