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Modern industrial applications require robots to be able to operate inunpredictable environments, and programs to be created with a minimal effort,as there may be frequent changes to the task. In this paper, we show thatgenetic programming can be effectively used to learn the structure of abehavior tree (BT) to solve a robotic task in an unpredictable environment.Moreover, we propose to use a simple simulator for the learning and demonstratethat the learned BTs can solve the same task in a realistic simulator, reachingconvergence without the need for task specific heuristics. The learned solutionis tolerant to faults, making our method appealing for real roboticapplications.

Learning Behavior Trees with Genetic Programming in Unpredictable Environments