Sim2real gap is non-monotonic with robot complexity for morphology-in-the-loop flapping wing design
This work addresses the sim2real transfer challenge for robot morphology design, particularly in flapping wing flight, though it appears incremental as it builds on existing automated design approaches.
The researchers investigated how the sim2real gap varies with robot complexity in flapping wing design, finding that the gap changes non-monotonically with complexity, suggesting certain morphology details can narrow the gap more effectively.
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.