# A Survey of Behavior Trees in Robotics and AI

arXiv: https://arxiv.org/abs/2005.05842 · HTML: https://mlsanity.com/paper/2005.05842/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2020.

## Built on by (most cited first, 30 of 55)

- [A Survey on Large Language Model-Based Game Agents](https://mlsanity.com/paper/2404.02039/) (2024)
- [ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning](https://mlsanity.com/paper/2406.19741/) (2024)
- [Robot Behavior-Tree-Based Task Generation with Large Language Models](https://mlsanity.com/paper/2302.12927/) (2023)
- [Learning Hierarchical Interactive Multi-Object Search for Mobile Manipulation](https://mlsanity.com/paper/2307.06125/) (2023)
- [Behavior Trees in Robot Control Systems](https://mlsanity.com/paper/2203.13083/) (2022)
- [Combining Planning and Learning of Behavior Trees for Robotic Assembly](https://mlsanity.com/paper/2103.09036/) (2021)
- [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)
- [An expressiveness hierarchy of Behavior Trees and related architectures](https://mlsanity.com/paper/2104.07919/) (2021)
- [Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems](https://mlsanity.com/paper/2201.04204/) (2022)
- [KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems](https://mlsanity.com/paper/2209.02886/) (2022)
- [TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency](https://mlsanity.com/paper/2502.09379/) (2025)
- [Learning of Parameters in Behavior Trees for Movement Skills](https://mlsanity.com/paper/2109.13050/) (2021)
- [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)
- [A Unified Framework for Real-Time Failure Handling in Robotics Using Vision-Language Models, Reactive Planner and Behavior Trees](https://mlsanity.com/paper/2503.15202/) (2025)
- [Continuous-Time Behavior Trees as Discontinuous Dynamical Systems](https://mlsanity.com/paper/2109.01575/) (2021)
- [BVR Gym: A Reinforcement Learning Environment for Beyond-Visual-Range Air Combat](https://mlsanity.com/paper/2403.17533/) (2024)
- [A survey of air combat behavior modeling using machine learning](https://mlsanity.com/paper/2404.13954/) (2024)
- [HARMONIC: A Framework for Explanatory Cognitive Robots](https://mlsanity.com/paper/2409.18037/) (2024)
- [RoboNeuron: A Middle-Layer Infrastructure for Agent-Driven Orchestration in Embodied AI](https://mlsanity.com/paper/2512.10394/) (2025)
- [WorldMind: Decoupled Game World Model for State-Aware NPC Behavior](https://mlsanity.com/paper/2608.21439/) (2026)
- [Procedural Content Generation using Behavior Trees (PCGBT)](https://mlsanity.com/paper/2107.06638/) (2021)
- [EvolvingBehavior: Towards Co-Creative Evolution of Behavior Trees for Game NPCs](https://mlsanity.com/paper/2209.01020/) (2022)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications](https://mlsanity.com/paper/2309.15049/) (2023)
- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Vision-Language-Policy Model for Dynamic Robot Task Planning](https://mlsanity.com/paper/2512.19178/) (2025)
- [Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning](https://mlsanity.com/paper/2603.06084/) (2026)

## Abstract (arXiv)

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.