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 …

Effective motion planning is an indispensable prerequisite for the optimal performance of
robotic manipulators in any task. In this regard, the research and application of …

The current era is marked by an accelerated digitization of manufacturing processes, with
robotic systems increasingly integrated into various workflows. Yet, despite significant …

While AI-driven automation can increase the performance and safety of systems, humans
should not be replaced in safety-critical systems but should be integrated to collaborate and …

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

Ensuring the health and safety of human operators is paramount in manufacturing,
particularly in human-robot collaborative environments. In this paper, we present a deep …

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 …