Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

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) …

Task automation of surgical robot has the potentials to improve surgical efficiency. Recent
reinforcement learning (RL) based approaches provide scalable solutions to surgical …

Multi-agent reinforcement learning (MARL) has been a rapidly evolving field. This paper
presents a comprehensive survey of MARL and its applications. We trace the historical …

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 …

Surgical robot task automation has recently attracted great attention due to its potential to
benefit both surgeons and patients. Reinforcement learning (RL) based approaches have …

Flexible Endoscopes (FEs) for colonoscopy present several limitations due to their inherent
complexity, resulting in patient discomfort and lack of intuitiveness for clinicians. Robotic FEs …

In the field of multi-agent autonomous transportation, such as automated payload delivery or
highway on-ramp merging, agents routinely exchange knowledge to optimize their shared …

Surgical context inference has recently garnered significant attention in robot-assisted
surgery as it can facilitate workflow analysis, skill assessment, and error detection. However …