Representing robot/environment interactions using probabilities: the "beam in the bin" experiment

Abstract

The paper presents a very simple robotic experiment to illustrate how probabilistic reasoning may be used for sensory motor systems. We show how our robot may learn internal representations of its interactions with the environment, how it may predict the sensory result of a given action, how it may generate motor command to reach a wished sensory situation, how it may recognize different status and novel conditions and finally how it may behave consistently to simultaneously explore and exploit its environment.