Learning forward models for robots

Abstract

Forward models enable a robot to predict the ef-fects of its actions on its own motor system and its environment. This is a vital aspect of intelligent be-haviour, as the robot can use predictions to decide the best set of actions to achieve a goal. The ability to learn forward models enables robots to be more adaptable and autonomous; this paper describes a system whereby they can be learnt and represented as a Bayesian network. The robot's motor system is controlled and explored using `motor babbling'. Feedback about its motor system comes from com-puter vision techniques requiring no prior informa-tion to perform tracking. The learnt forward model can be used by the robot to imitate human move-ment. 1