Hierarchical MMC Networks as a manipulable body model

A cognitive control system for a walking robot should be able to solve from simple reactive tasks up to complex tasks, including tasks which need cognitive capabilities and setting up plans. Planning ahead involves some kind of internal representation: most important a model of the own body. Considering planning as mental simulation, this model must be fully functional: it is constrained in the same way as the body itself and it can move and be used in the same way as the body. This model can then be used to try out movements mentally without doing the action in reality. For this purpose it must be possible to decouple the body itself from the action controlling modules to use the original controllers for control of the internal representations. In this publication we introduce a hierarchical model, implemented as an recurrent neural network based on the MMC principle.