Increased demand for less invasive procedures has accelerated the adoption of Intraluminal
Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and …

Automation holds the potential to assist surgeons in robotic interventions, shifting their
mental work load from visuomotor control to high level decision making. Reinforcement …

Recent advances in reinforcement learning (RL) have increased the promise of introducing
cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS) …

Surgical robot automation has attracted increasing research interest over the past decade,
expecting its potential to benefit surgeons, nurses and patients. Recently, the learning …

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 emerging as a promising approach to generate
adaptive behaviors for robotic platforms. However, a major drawback of using DRL is the …

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 …

While the traditional control of surgical robots relies on fully manual teleoperations, human-
robot collaborative systems promise to address issues such as workspace constrains and …

Reinforcement learning is still struggling with solving long-horizon surgical robot tasks which
involve multiple steps over an extended duration of time due to the policy exploration …

Surgical robotics is a rapidly evolving field that is transforming the landscape of surgeries.
Surgical robots have been shown to enhance precision, minimize invasiveness, and …