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In modern industrial collaborative robotic applications, it is desirable tocreate robot programs automatically, intuitively, and time-efficiently.Moreover, robots need to be controlled by reactive policies to face theunpredictability of the environment they operate in. In this paper we propose aframework that combines a method that learns Behavior Trees (BTs) fromdemonstration with a method that evolves them with Genetic Programming (GP) forcollaborative robotic applications. The main contribution of this paper is toshow that by combining the two learning methods we obtain a method that allowsnon-expert users to semi-automatically, time-efficiently, and interactivelygenerate BTs. We validate the framework with a series of manipulationexperiments. The BT is fully learnt in simulation and then transferred to areal collaborative robot.

A Framework for Learning Behavior Trees in Collaborative Robotic Applications