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

TL;DR

This paper introduces Reconfigurable Behavior Trees (RBTs), an extension of traditional behavior trees that incorporates physical constraints and sensory feedback for dynamic, real-time decision making in robotics.

Contribution

The paper presents RBTs, a novel framework that integrates environmental constraints and sensory data into behavior trees for improved robotic control.

Findings

RBTs effectively handle environmental changes during task execution.

RBTs enable online monitoring of robotic tasks.

Experimental results demonstrate RBTs' suitability for robotic applications.

Abstract

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.