Model-based reinforcement learning for robot-based laser material processing

Abstract

Articulated robotic arms in laser material processing require precise motion planning. Traditional motion planning methods face challenges in trajectory accuracy. This study demonstrates model-based reinforcement learning as an effective approach for motion planning of these robotic arms. The process involves training a neural network trajectory model based on Pilz Industrial Motion Planner, followed by training an agent to optimize motion by adjusting joint velocities.The study compares Proximal Policy Optimization and Soft Actor-Critic algorithms to the baseline Pilz motion plan. Results show that model-based reinforcement learning improves accuracy in x-direction, reducing mean absolute error to 1.75 × 10−3 m from 6.37 × 10−3 m. However, it slightly increases z-direction mean absolute error, from 6 × 10−6 m to 2.5 × 10−4 m. This leads to an increase in on-surface beam radius, from 2.9 × 10−5 m to 3.3 × 10−5 m, and decrease in peak intensity of 22.77 % compared to baseline.These results highlight reinforcement learning’s potential to enhance trajectory accuracy in motion planning, advancing robot-based laser material processing.