  Behavior Trees constitute a widespread AI tool which has been successfully
spun out in robotics. Their advantages include simplicity, modularity, and
reusability of code. However, Behavior Trees remain a high-level decision
making engine; control features cannot be easily integrated. This paper
proposes the Reconfigurable Behavior Trees (RBTs), an extension of the
traditional BTs that considers physical constraints from the robotic
environment in the decision making process. We endow RBTs with continuous
sensory information that permits the online monitoring of the task execution.
The resulting stimulus-driven architecture is capable of dynamically handling
changes in the executive context while keeping the execution time low. The
proposed framework is evaluated on a set of robotic experiments. The results
show that RBTs are a promising approach for robotic task representation,
monitoring, and execution.
