Learning human tracking and intercepting skill

Robot tracking and intercepting fast-maneuvering object is a classical and important issue. Many research results were published in recent years. Most of them employed model-based methods which require robot´s model in advance. However, it is difficult and time-consuming to obtain robot´s mathematical model. In this paper, we present a novel approach which needs no mathematical model. The proposed approach is based on learning tracking strategy from human beings. With human´s demonstrations, the robot can learn and abstract human tracking and intercepting skill using cascade neural network. Preliminarily simulation results attest the feasibility of this novel approach. Furthermore, experiment is done on a real-time human face tracking system and the results verify the validity and efficiency of the approach.