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Morphology of a robot design is important to its ability to achieve a statedgoal and therefore applying machine learning approaches that incorporatemorphology in the design space can provide scope for significant advantage. Ourstudy is set in a domain known to be reliant on morphology: flapping wingflight. We developed a parameterised morphology design space that drawsfeatures from biological exemplars and apply automated design to produce a setof high performance robot morphologies in simulation. By performing sim2realtransfer on a selection, for the first time we measure the shape of the realitygap for variations in design complexity. We found for the flapping wing thatthe reality gap changes non-monotonically with complexity, suggesting thatcertain morphology details narrow the gap more than others, and that suchdetails could be identified and further optimised in a future end-to-endautomated morphology design process.

Sim2real gap is non-monotonic with robot complexity for morphology-in-the-loop flapping wing design