Reinforcement Learning-Based Optimization of Robotic Motion for High-Frequency Brazing Task of Copper Tube Joining*

Achieving consistent performance, and highquality brazed joints for copper tubes poses significant challenges due to the intricate interplay of thermal dynamics, material properties, and robot motion control. This paper presents a reinforcement learning (RL)-based framework for optimizing robotic trajectories and parameters in manufacturing brazing applications. The learned policy successfully guided the endeffector near the target region, as verified by the final pose.