Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

COVID-19 pandemic has created a lot of turmoil in medical teaching, the magnitude of
impact is many folds in the subject of anatomy, as it is practical based. A major challenge for …

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

Path Following and Collision Avoidance, be it for unmanned surface vessels or other
autonomous vehicles, are two fundamental guidance problems in robotics. For many …

Both conceptual modeling and machine learning have long been recognized as important
areas of research. With the increasing emphasis on digitizing and processing large amounts …

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed
can increase user participation and motor learning and allow for more effective gait …

Data-driven models can reduce the number of hardware sensors in a process plant by
acting as low-cost substitutes for hardware sensors. Since some data-driven models have …

The single train trajectory optimization, also known as speed profile optimization (SPO), is a
traditional problem to minimize the traction energy consumption of trains. As a kind of …

In this study, path planning and stabilization of humanoids are carried out in an uneven path
and dynamic environment. The importance of the work focuses on avoiding local minima …