# An Extended Convergence Result for Behaviour Tree Controllers

arXiv: https://arxiv.org/abs/2308.08994 · HTML: https://mlsanity.com/paper/2308.08994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 17 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [A Survey of Behavior Trees in Robotics and AI](https://mlsanity.com/paper/2005.05842/) (2020)
- [Towards Blended Reactive Planning and Acting using Behavior Trees](https://mlsanity.com/paper/1611.00230/) (2016)
- [Representing Robot Task Plans as Robust Logical-Dynamical Systems](https://mlsanity.com/paper/1908.01896/) (2019)
- [On modularity in reactive control architectures, with an application to formal verification](https://mlsanity.com/paper/2008.12515/) (2020)
- [Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees](https://mlsanity.com/paper/1809.10283/) (2018)
- [An expressiveness hierarchy of Behavior Trees and related architectures](https://mlsanity.com/paper/2104.07919/) (2021)
- [Learning Dynamic-Objective Policies from a Class of Optimal Trajectories](https://mlsanity.com/paper/1902.10139/) (2019)
- [Continuous-Time Behavior Trees as Discontinuous Dynamical Systems](https://mlsanity.com/paper/2109.01575/) (2021)

## Built on by (most cited first, 1 of 1)

- [Behavior Trees Enable Structured Programming of Language Model Agents](https://mlsanity.com/paper/2404.07439/) (2024)

## Abstract (arXiv)

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.