A model for inferring the intention in imitation tasks

Abstract

Robot imitation comprises a number of hard problems, one of the most difficult problems is the extraction of intention from a demonstration. A demonstration can almost always be interpreted in different ways, making it impossible for the imitating robot to find out what exactly it is that was demonstrated. We first attempt to set out the problem of intention reading. Next, we offer a computational model which implements a solution to intention reading. Our model needs repeated interactions between the demonstrator and the imitator. Through keeping a score about which interactions where successful, the imitating robot gradually builds a model which "understands" what the intent is of the demonstrator