The capability to behave autonomously is assumed to rely fundamentally on being embedded into the current situation and in the own body. While reactive systems seem sufficient to address these aspects to assure ones surviving in an unpredictable environment, they clearly lack cognitive capabilities as planning ahead: The latter requires internal models which represents the body and the environment and which can be used to mentally simulate behaviours before actually performing one of them. Initially, these models may have evolved in reactive systems to serve specific actions. Cognitive functions may have developed later exploiting the capabilities of these models.
We provide a neuronal network approach for such an internal model that can be used as a forward model, an inverse model and a sensor fusion model. It is integrated into a reactive control scheme of a walking machine, enabling the system to plan its actions by mentally simulating them.