Active robot learning of object properties

Abstract

We presents a method for a robot to autonomously learn hidden properties of an object using active interaction and outcome prediction. Using a simulator we generate hypotheses about an object's properties and predictions of the outcomes of robot actions. To determine which hypothesis model most accurately describes the object, we match the result of a real world action to the simulated outcomes. The simulation is also used to find the most informative action, minimising the total number of actions the robot needs to perform to model the object. The end result is a model accurately describing the physical properties of the real world object.