AI Enhanced Human-Robot Collaboration in Smart Manufacturing

In the era of Industry 4.0, smart manufacturing systems have witnessed unprecedented growth, ushering in a new era of productivity and flexibility. The integration of robots into manufacturing environments has been instrumental in achieving these advancements. However, the full potential of human-robot collaboration (HRC) is yet to be fully realized. This dissertation addresses novel approaches to enhance human-robot collaborative efficiency and reduce the burden of reprogramming through the application of reinforcement learning (RL)/ deep reinforcement learning (DRL) based task scheduling and robot learning from demonstration (LfD).