  Robots are used in more and more complex environments, and are expected to be
able to adapt to changes and unknown situations. The easiest and quickest way
to adapt is to change the control system of the robot, but for increasingly
complex environments one should also change the body of the robot -- its
morphology -- to better fit the task at hand. The theory of Embodied Cognition
states that control is not the only source of cognition, and the body,
environment, interaction between these and the mind all contribute as cognitive
resources. Taking advantage of these concepts could lead to improved
adaptivity, robustness, and versatility, however, executing these concepts on
real-world robots puts additional requirements on the hardware and has several
challenges when compared to learning just control. In contrast to the majority
of work in Evolutionary Robotics, Eiben argues for real-world experiments in
his `Grand Challenges for Evolutionary Robotics'. This requires robust hardware
platforms that are capable of repeated experiments which should at the same
time be flexible when unforeseen demands arise. In this paper, we introduce our
unique robot platform with self-adaptive morphology. We discuss the challenges
we have faced when designing it, and the lessons learned from real-world
testing and learning.
