An Approach Based on

Abstract

We present a probabilistic approach to learning robust models of human motion through imitation. The combination of hidden Markov model (HMM) and Gaussian mixture regression (GMR) allows us to extract redundancies across multiple demonstrations and build robust models to reproduce the dynamics of the observed movements. The approach is first compared with state-of-theart approaches by using generated trajectories, sharing similar characteristics to those of humans. Three applications on different types of robots are then presented. An experiment with the iCub humanoid robot, acquiring a bimanual dancing motion, is first presented to show that the system can cope with cyclic and crossing motions. An experiment with a seven-degrees of freedom (DoF) WAM robotic arm learning the motion of hitting a ball with a table tennis racket is presented to highlight the possibility to encode several movements in a single model. Finally, an experiment with a HOAP-3 humanoid robot holding a spoon and learning to feed the Robota humanoid robot is presented. It shows the capability of the system to handle several constraints simultaneously.