This paper presents a very simple robotic experiment to illustrate how probabilistic reasoning may be used for sensory-motor systems. We will 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 explore and exploit simultaneously its environment.