This paper analyses the strengths and weaknesses of self-organising approaches, such as evolutionary ro-botics, and direct design approaches, such as behav-iour-based controllers, for the production of autono-mous robots' controllers, and shows how the two approaches can be usefully combined. In particular, the paper proposes a method for encoding evolved neural-network based behaviours into motor schema-based controllers and then shows how these control-lers can be modified and combined to produce robots capable of solving new tasks. The method has been validated in the context of a collective robotics sce-nario in which a group of physically assembled simulated autonomous robots are requested to pro-duce different forms of coordinated behaviours (e.g., coordinated motion, walled-arena exiting, and light pursuing).
