The videos linked below demonstrate the gaits produced by neural network controllers designed using the ENSO neuroevolution method for a physical quadruped robot. These controllers were evaluated in a physical simulation of the robot for walking on flat ground (1) when all four legs of the robot are functional and (2) when one leg is disabled to simulate a real-world motor failure. The controllers produced in the first experiment were evaluated further for generalization by reducing the maximum speed of the motors and by initializing one of the legs with a large error. In each experiment, generalization was also tested by placing the robot on different surfaces. These experiments show that the evolved controllers generalize well and are more robust against faults than a hand-designed PID controller, demonstrating the potential of the ENSO approach for real-world applications.