Behaviour Trees for Evolutionary Robotics
This work addresses the challenge of interpretability and manual adaptation in evolutionary robotics for real-world deployment, though it is incremental as it applies an existing framework to a new robotic platform.
The paper tackled the problem of making evolved robotic behaviors more interpretable and adaptable when transferring from simulation to reality by applying Behavior Trees to a real flapping-wing micro air vehicle, achieving a 54% real-world success rate after user adaptation compared to 46% with a tuned user-defined controller.
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.