Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method

Abstract

Deep reinforcement learning (DRL) has achieved great success across multiple fields; however, in the field of robot control, the acquisition of large amounts of motion data from real robots is challenging. In this work, an algorithm is proposed to train a neural network model with a large amount of data in a simulated environment and then transfer the model to the real environment. Proximal policy optimization (PPO) is used to train the agent in simulator, and generative adversarial imitation learning (GAIL) is specified to transfer the model to the real world. The algorithm can guide the two-armed robot to complete the task well in the face of complex assembly tasks. A total of three tasks of different difficulties were set to test the performance of the algorithm. In a large experimental study, the proposed algorithm outperforms other algorithms, and the real robot arm completes the assembly task significantly faster than script and keyboard operations.