Behavior trees (BTs) are an optimally modular framework to assemble hierarchical hybrid control policies from a set of low-level control policies using a tree structure. Many robotic tasks are naturally decomposed into a hierarchy of control tasks, and modularity is a well-known tool for handling complexity, therefor behavior trees have garnered widespread usage in the robotics community. In this paper, we study the convergence of BTs, in the sense of reaching a desired part of the state space. Earlier results on BT convergence were often tailored to specific families of BTs, created using different design principles. The results of this paper generalize the earlier results and also include new cases of cyclic switching not covered in the literature.

翻译：行为树（BTs）是一种最优模块化框架，通过树状结构从一组底层控制策略中组装出层次化混合控制策略。许多机器人任务自然分解为控制任务的层次结构，而模块化是处理复杂性的公认工具，因此行为树在机器人领域得到了广泛应用。本文研究了行为树在到达状态空间期望区域意义上的收敛性。此前关于行为树收敛性的结果通常针对采用不同设计原则创建的特定行为树族。本文的研究结果对前期成果进行了推广，并涵盖了文献中尚未涉及的循环切换新情形。