This article describes how the SGOCE paradigm has been used within the context of a "minimal simulation" strategy to evolve neural networks controlling locomotion and obstacle-avoidance in a 6-legged robot. A standard genetic algorithm has been used to evolve developmental programs according to which recurrent networks of leaky-integrators neurons were grown in a user-provided developmental substrate and were connected to the robot's sensors and actuators. Specific grammars have been used to limit the complexity of the developmental programs and of the corresponding neural controllers. Such controllers have been first evolved through simulation and then successfully downloaded on the real robot.