Automatic Synthesis of Controllers for Real Robots Based on Preprogrammed Behaviors

We present a novel methodology for the synthesis of behavioral control for real robotic hardware. In our approach, neural controllers decide when different preprogrammed behaviors should be active during task execution. We evaluate our approach in a double T-maze task carried out by an e-puck robot. We compare results obtained in our setup with results obtained in a traditional evolutionary robotics setup where the neural controller has direct control over the robot’s actuators. The results show that the combination of preprogrammed and evolved control offers two key benefits over a traditional evolutionary robotics approach: (i) solutions are synthesized faster and achieve a higher performance, and (ii) solutions synthesized in simulation maintain their performance when transferred to real robotic hardware.