  Morphology of a robot design is important to its ability to achieve a stated
goal and therefore applying machine learning approaches that incorporate
morphology in the design space can provide scope for significant advantage. Our
study is set in a domain known to be reliant on morphology: flapping wing
flight. We developed a parameterised morphology design space that draws
features from biological exemplars and apply automated design to produce a set
of high performance robot morphologies in simulation. By performing sim2real
transfer on a selection, for the first time we measure the shape of the reality
gap for variations in design complexity. We found for the flapping wing that
the reality gap changes non-monotonically with complexity, suggesting that
certain morphology details narrow the gap more than others, and that such
details could be identified and further optimised in a future end-to-end
automated morphology design process.
