Deep Reinforcement Learning for Robotic Control in High-Dexterity Assembly Tasks - A Reward Curriculum Approach

For years, the fully-automated robotic assembly has been a highly sought-after technology in large-scale manufacturing. Yet it still struggles to find widespread implementation in industrial environments. Traditional programming has so far proven to be insufficient in providing the required flexibility and dexterity to solve complex assembly tasks. Research in robotic control using deep reinforcement learning (DRL) advances quickly, however, the transfer to real-world applications in industrial settings is lagging behind. In this study, we apply DRL for robotic motion control to a multi-body contact automotive assembly task. Our focus lies on optimizing the final performance on the real-world setup. We propose a reward-curriculum learning approach in combination with domain randomization to obtain both force-sensitivity and generalizability of the controller's performance. We train the agent exclusively in simulation and successfully perform the Sim-to-Real transfer. Finally, we evaluate the controller's performance and robustness on an industrial setup and reflect its adherence to the high standards of automotive production.