Quantifying Self-Organizing Behavior of Autonomous Robots

Abstract

Introduction In recent years research in autonomous robots has been more and more successful in developing algorithms for generating behavior from a generic taskindependent objective. Examples are intrinsic motivations for artificial curiosity, empowerment, homeokinesis, and maximizing predictive information. Independently of its origin, it would be useful to quantify behavior, in order to objectively compare algorithms with each other or even with human or animal movements. We investigate different methods for extracting characteristic measures based on information theoretic quantities. Example Behaviors We consider behaviors from a hexapod robot with 18DoF and from a snake robot with 14DoF when controlled by a simplified predictive information maximizing controller (Der and Martius, 2013). The behavior is generated by an adaptive coupling between sensors and motors in a purely reactive manner ‐ without a central pattern generator nor internal recurrences. In Fig 1 two example behaviors of the snake are shown which we uses here as a brief illustration. The data consists of 10 6 steps of joint position sensors.