Generation of Reaching Motions for Flat Cable Insertion Task Using Simulation Learning and Domain Adaptation for Industrial Robots

Abstract

In this paper, we propose a method for generating reaching motions for the insertion of flat cables. Despite the need to insert flat cables into sockets in the circuit assembly of various electronic devices, there has been little research on automating the insertion flat cables that are already fixed on one side to the circuit board. In this regard, we focus on the generation of reaching motions in the posture for grasping such flat cables. Our method uses deep reinforcement learning in a simulation environment, and the features extracted from the image and the pose of the manipulator are used as states. For the transfer from the simulation environment to the real-world environment, we use a CycleGAN-based domain adaptation method. We conducted experiments under several different conditions in a real-world environment to verify operation of the trained agent. The results demonstrated that the success rate of the generated reaching motions exceeded 70% under all conditions.