  Robotic systems for manipulation tasks are increasingly expected to be easy
to configure for new tasks. While in the past, robot programs were often
written statically and tuned manually, the current, faster transition times
call for robust, modular and interpretable solutions that also allow a robotic
system to learn how to perform a task. We propose the method Behavior-based
Bayesian Optimization and Planning (BeBOP) that combines two approaches for
generating behavior trees: we build the structure using a reactive planner and
learn specific parameters with Bayesian optimization. The method is evaluated
on a set of robotic manipulation benchmarks and is shown to outperform
state-of-the-art reinforcement learning algorithms by being up to 46 times
faster while simultaneously being less dependent on reward shaping. We also
propose a modification to the uncertainty estimate for the random forest
surrogate models that drastically improves the results.
