Model accelerated reinforcement learning for high precision robotic assembly

Abstract

Peg-in-hole assembly with narrow clearance is a typical robotic contact-rich task in industrial manufacturing. Robot learning allows robots to directly acquire the assembly skills for this task without modeling and recognizing the complex contact states. However, learning such skills is still challenging for robot because of the difficulties in collecting massive transitions data and transferring skills to new tasks, which inevitably leads to low training efficiency. This paper formulated the assembly task as a Markov decision process, and proposed a model accelerated reinforcement learning method to efficiently learn assembly policy. In this method, the assembly policy is learned with the maximum entropy reinforcement learning framework and executed with an impedance controller, which ensures exploration efficiency meanwhile allows transferring skills between tasks. To reduce sample complexity and improve training efficiency, the proposed method learns the environment dynamics with Gaussian Process while training policy, then, the learned dynamic model is utilized to improve target value estimation and generate virtual data to argument transition samples. This method can robustly learn assembly skills while minimizing real-world interaction requirements which makes it suitable for realistic assembly scenarios. To verify the proposed method, experiments on an industrial robot are conducted, and the results demonstrate that the proposed method improves the training efficiency by 31% compared with the method without model acceleration and the learned skill can be transferred to new tasks to accelerate the training for new policies.