While internal models are a prerequisite for

higher-level function, they have to be grounded in lowerlevel

function serving sensorimotor control. In this paper we

introduce an internal body model for the control of a hexapod

walker. The internal model deals with a highly complex robotic

structure of 22 degrees of freedom and coordinates the single

joint movements to achieve an overall stable and adaptive

walking behavior. It is implemented as a hierarchical recurrent

neural network consisting of different levels of abstraction

which are tightly intertwined. We demonstrate the feasibility

of the concept by applying the model to a simulated robot

and show how the different levels of the body model interact

and how this allows to scale the model even further. While

the internal model is used in this context explicitly for motor

control, it is also a predictive model and can be applied for

sensor fusion. We discuss how in this way such an internal

model offers the flexibility to be utilized in motor control and

to be used for planning ahead by a cognitive expansion of the

movement controller.