Measurement of Robot Similarity to Determine the Best Demonstrator for Imitation in a Group of Heterogeneous Robots

Imitation is not only a powerful means to drastically downsize the exploration space when learning behavior. It also helps to align the learning efforts of a robot group towards a common goal. However, one prerequisite in imitation, the decision of which robot to imitate, is often factored out in current research. In our work we address this question by providing a means to measure the similarity between two robots. Based on this similarity a robot can choose which robot to imitate. The affinity of two robots with respect to imitation is most reasonably measured by calculating their behavioral difference, since the goal of imitation is learning new behavior. This is accomplished by each robot individually constructing an Affordance Network which is a Bayesian network upon its conditional affordance probabilities in the environment. An affordance represents the interaction possibilities an object provides to the robot. These Affordance Networks are then compared with a new metric.