# Behavior Trees in Robot Control Systems

arXiv: https://arxiv.org/abs/2203.13083 · HTML: https://mlsanity.com/paper/2203.13083/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 65 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [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)
- [Learning of Behavior Trees for Autonomous Agents](https://mlsanity.com/paper/1504.05811/) (2015)
- [On modularity in reactive control architectures, with an application to formal verification](https://mlsanity.com/paper/2008.12515/) (2020)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [On the Implementation of Behavior Trees in Robotics](https://mlsanity.com/paper/2106.15227/) (2021)
- [A Framework for Constrained and Adaptive Behavior-Based Agents](https://mlsanity.com/paper/1506.02312/) (2015)
- [Learning Behavior Trees with Genetic Programming in Unpredictable Environments](https://mlsanity.com/paper/2011.03252/) (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)
- [Optimized Execution of PDDL Plans using Behavior Trees](https://mlsanity.com/paper/2101.01964/) (2021)
- [Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions](https://mlsanity.com/paper/2008.09427/) (2020)
- [Continuous-Time Behavior Trees as Discontinuous Dynamical Systems](https://mlsanity.com/paper/2109.01575/) (2021)
- [Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms](https://mlsanity.com/paper/1909.04368/) (2019)
- [Simulation Results on Selector Adaptation in Behavior Trees](https://mlsanity.com/paper/1606.09219/) (2016)
- [User Experience of the CoSTAR System for Instruction of Collaborative Robots](https://mlsanity.com/paper/1703.07890/) (2017)

## Built on by (most cited first, 13 of 13)

- [RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World](https://mlsanity.com/paper/2412.00171/) (2024)
- [LLM-MARS: Large Language Model for Behavior Tree Generation and NLP-enhanced Dialogue in Multi-Agent Robot Systems](https://mlsanity.com/paper/2312.09348/) (2023)
- [Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation](https://mlsanity.com/paper/2603.02623/) (2026)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat](https://mlsanity.com/paper/2403.17533/) (2024)
- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Temporal and Semantic Evaluation Metrics for Foundation Models in Post-Hoc Analysis of Robotic Sub-tasks](https://mlsanity.com/paper/2403.17238/) (2024)
- [Execution Semantics of Behavior Trees in Robotic Applications](https://mlsanity.com/paper/2408.00090/) (2024)
- [Progress Constraints for Reinforcement Learning in Behavior Trees](https://mlsanity.com/paper/2602.06525/) (2026)
- [CABTO: Context-Aware Behavior Tree Grounding for Robot Manipulation](https://mlsanity.com/paper/2603.16809/) (2026)
- [Contract-Grounded Behavior Tree Synthesis via Coding Agents](https://mlsanity.com/paper/2607.12220/) (2026)
- [Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems](https://mlsanity.com/paper/2609.29228/) (2026)

## Abstract (arXiv)

In this paper we will give a control theoretic perspective on the research area of behavior trees in robotics. The key idea underlying behavior trees is to make use of modularity, hierarchies and feedback, in order to handle the complexity of a versatile robot control system. Modularity is a well-known tool to handle software complexity by enabling development, debugging and extension of separate modules without having detailed knowledge of the entire system. A hierarchy of such modules is natural, since robot tasks can often be decomposed into a hierarchy of sub-tasks. Finally, feedback control is a fundamental tool for handling uncertainties and disturbances in any low level control system, but in order to enable feedback control on the higher level, where one module decides what submodule to execute, information regarding progress and applicability of each submodule needs to be shared in the module interfaces. We will describe how these three concepts come to use in theoretical analysis, practical design, as well as extensions and combinations with other ideas from control theory and robotics.