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Behavior Trees constitute a widespread AI tool which has been successfullyspun out in robotics. Their advantages include simplicity, modularity, andreusability of code. However, Behavior Trees remain a high-level decisionmaking engine; control features cannot be easily integrated. This paperproposes the Reconfigurable Behavior Trees (RBTs), an extension of thetraditional BTs that considers physical constraints from the roboticenvironment in the decision making process. We endow RBTs with continuoussensory information that permits the online monitoring of the task execution.The resulting stimulus-driven architecture is capable of dynamically handlingchanges in the executive context while keeping the execution time low. Theproposed framework is evaluated on a set of robotic experiments. The resultsshow that RBTs are a promising approach for robotic task representation,monitoring, and execution.

Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features