title: Behavior Trees for Evolutionary Robotics.
abstract: Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior 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-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g 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 optimized behavior 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.
authors.forename: Kirk Y W
authors.initials: KY
authors.lastname: Scheper
authors.forename: Sjoerd
authors.initials: S
authors.lastname: Tijmons
authors.forename: Cornelis C
authors.initials: CC
authors.lastname: de Visser
authors.affiliation: Delft University of Technology.
authors.forename: Guido C H E
authors.initials: GC
authors.lastname: de Croon
mesh_terms: D005075:Biological Evolution; D016571:Neural Networks, Computer; D012371:Robotics
keywords: Behavior tree; evolutionary robotics; micro air vehicle; reality gap