An Automatic Robot Skills Learning System from Robot’s Real-World Demonstrations

Abstract

In order to avoid complicated programming difficulties in robot control, we propose an automatic robot learning system which can learn skills from real-world demonstrations by robot. The system utilizes RGB-D camera to record one robot's demonstrations and then the demonstration data are processed and transferred into robot simulation environment. The policy model is trained entirely in simulation with the advantage of avoiding safety problem which is the key difficulty of real-world training. Then the learned policy is automatically transferred to another robot to reproduce the demonstrated skills. The experiments show that the system could automatically finish entire learning process from recording the robot demonstrations to applying the learned policy to another robot. And with the selected policy learning method, the robot could not only acquire skills but outperform the demonstrator.