Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation
environments, applying RL to real-world applications still faces many challenges. A major …

Additive manufacturing (AM) technology has achieved universal application in a great
number of fields, such as aerospace, medicine, and military industry. As a significant factor …

Safety is one of the biggest concerns to applying reinforcement learning (RL) to the physical
world. In its core part, it is challenging to ensure RL agents persistently satisfy a hard state …

Due to the trial-and-error nature, it is typically challenging to apply RL algorithms to safety-
critical real-world applications, such as autonomous driving, human-robot interaction, robot …

Seamless human-robot manipulation in close proximity relies on accurate forecasts of
human motion. While there has been significant progress in learning forecast models at …

Reinforcement Learning (RL) algorithms have shown tremendous success in simulation
environments, but their application to real-world problems faces significant challenges, with …

Trajectory generation in confined environment is crucial for wide adoption of intelligent robot
manipulators. In this paper, we propose a novel motion planning approach for redundant …

Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety
guarantees hampers its real-world applicability. In particular, explorations during learning …

The advent of autonomous mobile robots has spurred research into efficient trajectory
planning methods, particularly in dynamic environments with varied obstacles. This study …

Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across
diverse applications, ranging from image classification to control. Verifying specific input …