Tan

In this paper, we propose to use an adaptive control method as the basis of a reinforcement learning algorithm for robotic imitation learning. In the learning stage, robots use adaptive control method-based reinforcement learning algorithm to learn the parameters of dynamical systems. In the generation stage, robots use the learned dynamic system parameters and the pre-defined controller to drive the configuration states of the robot to move along desired state trajectories. One simu-lation experiment and one practical experiment on a robot are carried out to validate the effectiveness of our algorithm. The experimental results validate that the learning of the system parameters converges very fast and the learning results can improve the system performance of generating similar motion trajectories.