Autonomous navigation for robot-assisted intraluminal and endovascular procedures: A systematic review
Increased demand for less invasive procedures has accelerated the adoption of Intraluminal
Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and …
Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and …
[HTML][HTML] A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going
Abstract Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive
surgical approaches, offering patient benefits such as smaller incisions, less pain, and …
surgical approaches, offering patient benefits such as smaller incisions, less pain, and …
Sim-to-real transfer for visual reinforcement learning of deformable object manipulation for robot-assisted surgery
Automation holds the potential to assist surgeons in robotic interventions, shifting their
mental work load from visuomotor control to high level decision making. Reinforcement …
mental work load from visuomotor control to high level decision making. Reinforcement …
LapGym-an open source framework for reinforcement learning in robot-assisted laparoscopic surgery
Recent advances in reinforcement learning (RL) have increased the promise of introducing
cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS) …
cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS) …
Guided reinforcement learning with efficient exploration for task automation of surgical robot
Task automation of surgical robot has the potentials to improve surgical efficiency. Recent
reinforcement learning (RL) based approaches provide scalable solutions to surgical …
reinforcement learning (RL) based approaches provide scalable solutions to surgical …
Human-in-the-loop embodied intelligence with interactive simulation environment for surgical robot learning
Surgical robot automation has attracted increasing research interest over the past decade,
expecting its potential to benefit surgeons, nurses and patients. Recently, the learning …
expecting its potential to benefit surgeons, nurses and patients. Recently, the learning …
[PDF][PDF] Formally Explaining Neural Networks within Reactive Systems
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 …
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …
Analyzing Adversarial Inputs in Deep Reinforcement Learning
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 …
machine learning due to its successful applications to real-world and complex systems …
Constrained reinforcement learning for robotics via scenario-based programming
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 …
variety of robotic applications. A natural consequence is the adoption of this paradigm for …
Autonomous blood suction for robot-assisted surgery: A sim-to-real reinforcement learning approach
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 …
promising results in automating various surgical sub-tasks. While most of these studies …