Learning Motion from Demonstration for Differential Drive Mobile Robot

Abstract

In this paper, we present new Learning from Demonstration ((LfD) - based algorithm that generalizes and extracts relevant features of desired motion trajectories for differential drive mobile robots. The algorithm is tested through series of simulations and real world experiments in which desired task is demonstrated by the human teacher while teleoperating the mobile robot in the working environment. The human teacher shows the specifics of the new skill or motion behavior that robot needs to learn; the robot extracts information from demonstrations and builds the internal model based on the learning algorithm and in this paper we will refer to it as the Learning from Demonstration.