Digital twins and artificial intelligence have shown promise for improving the robustness,
responsiveness, and productivity of industrial systems. However, traditional digital twin …

With the emergence of Industry 5.0, the human-centric manufacturing paradigm requires
manufacturing equipment (robots, etc.) interactively assist human workers to deal with …

Deep reinforcement learning (RL) has shown promising results in the motion planning of
manipulators. However, no method guarantees the safety of highly dynamic obstacles, such …

Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their
potential for many real-world tasks. However, vanilla RL and most safe RL approaches do …

Nowadays, AI-based techniques, such as deep neural networks (DNNs), are widely
deployed in autonomous systems for complex mission requirements (eg, motion planning in …

Safety is a fundamental property for the real-world deployment of robotic platforms. Any
control policy should avoid dangerous actions that could harm the environment, humans, or …

This article presents a literature review of the past five years of studies using Deep
Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic …

Ensuring safety of reinforcement learning (RL) algorithms is crucial to unlock their potential
for many real-world tasks. However, vanilla RL does not guarantee safety. In recent years …

Surgical task automation in robotics can improve the outcomes, reduce quality-of-care
variance among surgeons and relieve surgeons' fatigue. Reinforcement learning (RL) …

Herein, we present a real-time multi-agent deep reinforcement learning model as a
disassembly planning framework for human–robot collaboration. This disassembly plan …