The estimation of the internal model of a robotic system results from the interaction of its morphology, sensors and
actuators, with a particular environment. Model learning techniques, based on supervised machine learning, are
widespread for determining the internal model. An important limitation of such approaches is that once a model has
been learnt, it does not behave properly when the robot morphology is changed. From this it follows that there must
exist a relationship between them. We propose a model for this correlation between the morphology and the internal
model parameters, so that a new internal model can be predicted when the morphological parameters are modified.
Di erent neural network architectures are proposed to address this high dimensional regression problem. A case
study is analyzed in detail to illustrate and evaluate the performance of the approach, namely, a pan-tilt robot head
executing saccadic movements. The best results are obtained for an architecture with parallel neural networks due
to the independence of its outputs. Theses results can have a great significance since the predicted parameters can
dramatically speed up the adaptation process following a change in morpholog
