  In this paper, we propose Belief Behavior Trees (BBTs), an extension to
Behavior Trees (BTs) that allows to automatically create a policy that controls
a robot in partially observable environments. We extend the semantic of BTs to
account for the uncertainty that affects both the conditions and action nodes
of the BT. The tree gets synthesized following a planning strategy for BTs
proposed recently: from a set of goal conditions we iteratively select a goal
and find the action, or in general the subtree, that satisfies it. Such action
may have preconditions that do not hold. For those preconditions, we find an
action or subtree in the same fashion. We extend this approach by including, in
the planner, actions that have the purpose to reduce the uncertainty that
affects the value of a condition node in the BT (for example, turning on the
lights to have better lighting conditions). We demonstrate that BBTs allows
task planning with non-deterministic outcomes for actions. We provide
experimental validation of our approach in a real robotic scenario and - for
sake of reproducibility - in a simulated one.
