This session is aimed at those research groups that develop and use novel artificial neural network architectures specially adapted to the necessities of real life interaction with dynamic environments. This includes neural networks for the implementation of sensing tasks, those dedicated to sensor fusion and higher level processes and finally those dedicated to the control and actuation part of a robot. An interesting subarea of this session would be the use of neural networks for behavior controllers and how to obtain and/or train networks taking into account the credit apportitioning problem and the fact that robots operate in very noisy environments that are very hard to simulate. Finally, a subject to be considered is the problem of combining networks corresponding to different behaviors into global controllers that provide advantages over monolithic solutions.