  Evolutionary Robotics allows robots with limited sensors and processing to
tackle complex tasks by means of sensory-motor coordination. In this paper we
show the first application of the Behaviour Tree framework to a real robotic
platform using the Evolutionary Robotics methodology. This framework is used to
improve the intelligibility of the emergent robotic behaviour as compared to
the traditional Neural Network formulation. As a result, the behaviour is
easier to comprehend and manually adapt when crossing the reality gap from
simulation to reality. This functionality is shown by performing real-world
flight tests with the 20-gram DelFly Explorer flapping wing Micro Air Vehicle
equipped with a 4-gram onboard stereo vision system. The experiments show that
the DelFly can fully autonomously search for and fly through a window with only
its onboard sensors and processing. The success rate of the optimised behaviour
in simulation is 88% and the corresponding real-world performance is 54% after
user adaptation. Although this leaves room for improvement, it is higher than
the 46% success rate from a tuned user-defined controller.
