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

Abstract Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive
surgical approaches, offering patient benefits such as smaller incisions, less pain, 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 neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …

Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide
variety of robotic applications. A natural consequence is the adoption of this paradigm for …

In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in
machine learning due to its successful applications to real-world and complex systems …

Cost functions are commonly employed in Safe Deep Reinforcement Learning (DRL).
However, the cost is typically encoded as an indicator function due to the difficulty of …