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

Abstract

Behavior Trees (BTs) constitute a widespread artificial intelligence tool that has been successfully adopted in robotics. Their advantages include simplicity, modularity, and reusability of code. However, Behavior Trees remain a highlevel decision making engine; control features cannot easily be integrated. This paper proposes Reconfigurable Behavior Trees (RBTs), an extension of the traditional BTs that incorporates sensed information coming from the robotic environment in the decision making process. We endow RBTs with continuous sensory data 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.