Learning nonlinear dynamical system for movement primitives

Xiaochuan Yin, Qijun Chen

Abstract: Learning from demonstration requires reproduction of a movement in the new situation. We present an approach based on dynamic movement primitives (DMP) and Gaussian mixture model (GMM) to learning the movement from demonstration. The original DMP model use only one demonstration to generate the dynamical system of motion primitive. Our work extend the generalization ability by capturing the characteristic of movement from several demonstrations of the same skill. We test our method on the mini-jerk trajectories of static and moving target and on data collected from nonholonomic mobile robot simulator. These experiments show that our method can improve the generalization of the basic motion primitives which is crucial to the application of imitation learning.