  Robots operating in the real world will experience a range of different
environments and tasks. It is essential for the robot to have the ability to
adapt to its surroundings to work efficiently in changing conditions.
Evolutionary robotics aims to solve this by optimizing both the control and
body (morphology) of a robot, allowing adaptation to internal, as well as
external factors. Most work in this field has been done in physics simulators,
which are relatively simple and not able to replicate the richness of
interactions found in the real world. Solutions that rely on the complex
interplay between control, body, and environment are therefore rarely found. In
this paper, we rely solely on real-world evaluations and apply evolutionary
search to yield combinations of morphology and control for our mechanically
self-reconfiguring quadruped robot. We evolve solutions on two distinct
physical surfaces and analyze the results in terms of both control and
morphology. We then transition to two previously unseen surfaces to demonstrate
the generality of our method. We find that the evolutionary search finds
high-performing and diverse morphology-controller configurations by adapting
both control and body to the different properties of the physical environments.
We additionally find that morphology and control vary with statistical
significance between the environments. Moreover, we observe that our method
allows for morphology and control parameters to transfer to previously-unseen
terrains, demonstrating the generality of our approach.
