LSTM Learn Policy from Dynamical System of Demonstration Motions for Robot Imitation Learning

Robot imitation learning has recently been studied extensively. There have been many methods for robot imitation learning. The main problem, however, is that the learned strategy is susceptible to external interference and thus deviates from the expected trajectory. In this paper we propose a method for robot learning human motion that combines the strengths of LSTM and dynamical system. Such an approach can learn policy effectively from dynamical system of motions while robot interacting with human demonstrators or other teachers. And this method effectively improves the ability of robot trajectory adjustment. Simulation experiments show that the proposed method which learn policy can effectively avoid the disturbance in the robot working environment.