Stochastic gesture production and recognition model for a humanoid robot

Abstract

Robot programming by demonstration (PbD) aims at developing adaptive and robust controllers to enable the robot to learn new skills by observing and imitating a human demonstration. While the vast majority of PbD works has focused on systems that learn a specific subset of tasks, our work explores the problem of recognizing, generalizing, and reproducing tasks in a unified mathematical framework. The approach makes abstraction of the task and dataset at hand to tackle the general issue of learning which of the features are the relevant ones to imitate. In this paper, we present an implementation of this framework to the determination of the optimal strategy to reproduce arbitrary gestures. The model is tested and validated on a humanoid robot, using recordings of the kinematics of the demonstrator's arm motion. The hand path and joint angle trajectories are encoded in hidden Markov models. The system uses the optimal prediction of the models to generate the reproduction of the motion.