Robot skill acquisition in assembly process using deep reinforcement learning

Abstract

Uncertain factors in environments restrict the intelligence level of industrial robots. Based on deep reinforcement learning, a skill-acquisition method is used to solve the posed problems of uncertainty in a complex assembly process. Under the frame of the Markov decision process, a quaternion sequence of the assembly process is represented. The reward function uses a trained classification model, which mainly recognizes whether the assembly is successful. The proposed skill-acquisition method is designed to make robots acquire assembly skills. The input of the model is the contact state of the assembly process, and the output is the robot action. The robot can complete the assembly by self-learning with little prior knowledge. To evaluate the performance of the proposed skill-acquisition method, simulations and real-world experiments were performed in a low-voltage apparatus assembly. The assembly success rate increases with the learning time. In the case of a random initial position and orientation, the assembly success rate was greater than 80% with little prior knowledge. The results show that the robot has a capability to complex assembly through skill acquisition.