  The main question this paper addresses is: What combination of a robot
controller and a learning method should be used, if the morphology of the
learning robot is not known in advance? Our interest is rooted in the context
of morphologically evolving modular robots, but the question is also relevant
in general, for system designers interested in widely applicable solutions. We
perform an experimental comparison of three controller-and-learner
combinations: one approach where controllers are based on modelling animal
locomotion (Central Pattern Generators, CPG) and the learner is an evolutionary
algorithm, a completely different method using Reinforcement Learning (RL) with
a neural network controller architecture, and a combination `in-between' where
controllers are neural networks and the learner is an evolutionary algorithm.
We apply these three combinations to a test suite of modular robots and compare
their efficacy, efficiency, and robustness. Surprisingly, the usual CPG-based
and RL-based options are outperformed by the in-between combination that is
more robust and efficient than the other two setups.
