Robustness to mechanical perturbations. To demonstrate that control of the robot by the optimized network is robust to mechanical perturbations, we plotted the fitness of the real and simulated robots as a function of speed and additive turning bias. The dots represent the fitness of the real robot averaged over four initial conditions. Error bars are plus-minus one standard deviation. The solid lines represent the fitness of the simulated robot averaged over the same initial conditions.
Dotted lines are plus-minus one standard deviation.
These results suggest that the real robot performs nearly identically to the simulated robot, and that both are robust to significant mechanical perturbations. To demonstrate that the network in the robot controls phototaxis by a simple form of active perception, we seek to extract a computational rule from the network which reveals the computations being performed. Mathematical extraction of computational rule. To extract a computational rule, we make use of the fact that the linear network equations are analytically soluble. Most important is the fact that it decays rapidly on the time scale of the behavior.