# Behavior Trees in Robotics and AI: An Introduction

arXiv: https://arxiv.org/abs/1709.00084 · HTML: https://mlsanity.com/paper/1709.00084/

## Key facts

- Cited by 107 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2017.

## Built on by (most cited first, 30 of 107)

- [Multi-Stage Cable Routing through Hierarchical Imitation Learning](https://mlsanity.com/paper/2307.08927/) (2023)
- [Principles and Guidelines for Evaluating Social Robot Navigation Algorithms](https://mlsanity.com/paper/2306.16740/) (2023)
- [GraSP: Graph-Structured Skill Compositions for LLM Agents](https://mlsanity.com/paper/2604.17870/) (2026)
- [Combining Evolution and Deep Reinforcement Learning for Policy Search: a Survey](https://mlsanity.com/paper/2203.14009/) (2022)
- [Bayesian Controller Fusion: Leveraging Control Priors in Deep Reinforcement Learning for Robotics](https://mlsanity.com/paper/2107.09822/) (2021)
- [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)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [AI Agent Systems: Architectures, Applications, and Evaluation](https://mlsanity.com/paper/2601.01743/) (2026)
- [HOI4ABOT: Human-Object Interaction Anticipation for Human Intention Reading Collaborative roBOTs](https://mlsanity.com/paper/2309.16524/) (2023)
- [Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions](https://mlsanity.com/paper/2405.07474/) (2024)
- [Skill-based Multi-objective Reinforcement Learning of Industrial Robot Tasks with Planning and Knowledge Integration](https://mlsanity.com/paper/2203.10033/) (2022)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [Affordable Generative Agents](https://mlsanity.com/paper/2402.02053/) (2024)
- [Agents Play Thousands of 3D Video Games](https://mlsanity.com/paper/2503.13356/) (2025)
- [A Comprehensive Review of Multi-Agent Reinforcement Learning in Video Games](https://mlsanity.com/paper/2509.03682/) (2025)
- [Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model](https://mlsanity.com/paper/2408.08282/) (2024)
- [LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation](https://mlsanity.com/paper/2411.00773/) (2024)
- [HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation](https://mlsanity.com/paper/2406.14655/) (2024)
- [FEAST: A Flexible Mealtime-Assistance System Towards In-the-Wild Personalization](https://mlsanity.com/paper/2506.14968/) (2025)
- [AEROS: A Single-Agent Operating Architecture with Embodied Capability Modules](https://mlsanity.com/paper/2604.07039/) (2026)
- [An expressiveness hierarchy of Behavior Trees and related architectures](https://mlsanity.com/paper/2104.07919/) (2021)
- [PlanSys2: A Planning System Framework for ROS2](https://mlsanity.com/paper/2107.00376/) (2021)
- [KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems](https://mlsanity.com/paper/2209.02886/) (2022)
- [Learning and Executing Re-usable Behaviour Trees from Natural Language Instruction](https://mlsanity.com/paper/2106.01650/) (2021)
- [Designing Behavior Trees from Goal-Oriented LTLf Formulas](https://mlsanity.com/paper/2307.06399/) (2023)
- [Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations](https://mlsanity.com/paper/2207.04188/) (2022)
- [SkiROS2: A skill-based Robot Control Platform for ROS](https://mlsanity.com/paper/2306.17030/) (2023)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning](https://mlsanity.com/paper/2406.00965/) (2024)
- [Designing Robust Cyber-Defense Agents with Evolving Behavior Trees](https://mlsanity.com/paper/2410.16383/) (2024)

## Abstract (arXiv)

A Behavior Tree (BT) is a way to structure the switching between different tasks in an autonomous agent, such as a robot or a virtual entity in a computer game. BTs are a very efficient way of creating complex systems that are both modular and reactive. These properties are crucial in many applications, which has led to the spread of BT from computer game programming to many branches of AI and Robotics. In this book, we will first give an introduction to BTs, then we describe how BTs relate to, and in many cases generalize, earlier switching structures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. Properties such as safety, robustness, and efficiency are important for an autonomous system, and we describe a set of tools for formally analyzing these using a state space description of BTs. With the new analysis tools, we can formalize the descriptions of how BTs generalize earlier approaches. We also show the use of BTs in automated planning and machine learning. Finally, we describe an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion.