This paper 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. Such controllers have been first evolved through simulation and then successfully downloaded on the real robot.