Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

The use of simulators in robotics research is widespread, underpinning the majority of recent
advances in the field. There are now more options available to researchers than ever before …

Driving safely requires multiple capabilities from human and intelligent agents, such as the
generalizability to unseen environments, the safety awareness of the surrounding traffic, and …

This review is dedicated to the advanced applications of robotic technologies in the
industrial field. Robotic solutions in areas with non-intensive applications are presented, and …

Robot-assisted and computer-integrated surgery provides innovative, minimally invasive
solutions to heal complex injuries and diseases. The dominant portion of these surgical …

Abstract Machine learning (ML), especially deep neural networks (DNNs) have been widely
used in various applications, including several safety-critical ones (eg autonomous driving) …

Robotic-assisted surgery is now well established in clinical practice and has become the
gold-standard clinical treatment option for several clinical indications. The field of robotic …

Autonomous surgical execution relieves tedious routines and surgeon's fatigue. Recent
learning-based methods, especially reinforcement learning (RL) based methods, achieve …

Traditional control and task automation have been successfully demonstrated in a variety of
structured, controlled environments through the use of highly specialized modeled robotic …

Safety is the major consideration in controlling complex dynamical systems using
reinforcement learning (RL), where the safety certificates can provide provable safety …