  For robots to handle the numerous factors that can affect them in the real
world, they must adapt to changes and unexpected events. Evolutionary robotics
tries to solve some of these issues by automatically optimizing a robot for a
specific environment. Most of the research in this field, however, uses
simplified representations of the robotic system in software simulations. The
large gap between performance in simulation and the real world makes it
challenging to transfer the resulting robots to the real world. In this paper,
we apply real world multi-objective evolutionary optimization to optimize both
control and morphology of a four-legged mammal-inspired robot. We change the
supply voltage of the system, reducing the available torque and speed of all
joints, and study how this affects both the fitness, as well as the morphology
and control of the solutions. In addition to demonstrating that this real-world
evolutionary scheme for morphology and control is indeed feasible with
relatively few evaluations, we show that evolution under the different hardware
limitations results in comparable performance for low and moderate speeds, and
that the search achieves this by adapting both the control and the morphology
of the robot.
