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

Understand

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.