Reinforcement Learning With Stereo-View Observation for Robust Electronic Component Robotic Insertion

Abstract

In modern manufacturing, assembly tasks are a major challenge for robotics. In the manufacturing industry, a wide range of insertion tasks can be found, from peg-in-hole insertion to electronic parts assembly. Robotic stations designed for this problem often use conventional hybrid force-position control to perform preprogrammed trajectories, such as e.g. a spiral path. However, electronic parts require more sophisticated techniques due to their complex geometry and susceptibility to damage. Production line assembly tasks require high robustness to initial position and rotation variations due to component grip imperfections. Robustness to partially obscured camera view is also mandatory due to multi stage assembly process. We propose a stereo-view method based on reinforcement learning (RL) for the robust assembly of electronic parts. Applicability of our method to real-world production lines is verified through test scenarios. Our approach is the most robust to applied perturbations of all tested methods and can potentially be transferred to environments unseen during learning.