Smart Robotic Arms Trained with RL for Assembly-Line Automation

Assembly-line automation requires robots that can react dynamically to a range of parts and extremely precise fits, but legacy systems that rely on fixed paths and teach-pendant programming are inflexible the reconfiguration force uncertainty, downtime is high during set-up for new products. In one example, a smart assembly framework leverages deep reinforcement learning to guide dual-Arm collaboration through visual pose estimation and force-Torque sensing as well as curriculum driven feedback-based training on tasks that span insertion, alignment, and handover. The results based on three product variants demonstrate success rate improvement from 88% up to 97%, cycle-Time reduction of 21 %, and performance drop of only 6 % for unseen geometries. Stimulation and analysis results show better stability than in worst-case conditions and lower peak contact forces, leading to a more reliable quality and less mechanical faults. The results indicate the potential for scalable adaptation with minimal retraining, which enables agile manufacturing and industrial smart-factory schemes.