  Industrial robots can solve very complex tasks in controlled environments,
but modern applications require robots able to operate in unpredictable
surroundings as well. An increasingly popular reactive policy architecture in
robotics is Behavior Trees but as with other architectures, programming time
still drives cost and limits flexibility. There are two main branches of
algorithms to generate policies automatically, automated planning and machine
learning, both with their own drawbacks. We propose a method for generating
Behavior Trees using a Genetic Programming algorithm and combining the two
branches by taking the result of an automated planner and inserting it into the
population. Experimental results confirm that the proposed method of combining
planning and learning performs well on a variety of robotic assembly problems
and outperforms both of the base methods used separately. We also show that
this type of high level learning of Behavior Trees can be transferred to a real
system without further training.
