  In evolutionary robotics, several approaches have been shown to be capable of
the joint optimisation of body-plans and controllers by either using only
evolution or combining evolution and learning. When working in rich
morphological spaces, it is common for offspring to have body-plans that are
very different from either of their parents, which can cause difficulties with
respect to inheriting a suitable controller. To address this, we propose a
framework that combines an evolutionary algorithm to generate body-plans and a
learning algorithm to optimise the parameters of a neural controller where the
topology of this controller is created once the body-plan of each offspring
body-plan is generated. The key novelty of the approach is to add an external
archive for storing learned controllers that map to explicit `types' of robots
(where this is defined with respect the features of the body-plan). By
inheriting an appropriate controller from the archive rather than learning from
a randomly initialised one, we show that both the speed and magnitude of
learning increases over time when compared to an approach that starts from
scratch, using three different test-beds. The framework also provides new
insights into the complex interactions between evolution and learning, and the
role of morphological intelligence in robot design.
