# Behavior Trees and State Machines in Robotics Applications

arXiv: https://arxiv.org/abs/2208.04211 · HTML: https://mlsanity.com/paper/2208.04211/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2022.

## Built on by (most cited first, 12 of 12)

- [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)
- [ADAPT: An Autonomous Forklift for Construction Site Operation](https://mlsanity.com/paper/2503.14331/) (2025)
- [Adaptive Manipulation using Behavior Trees](https://mlsanity.com/paper/2406.14634/) (2024)
- [Multimodal Behavior Tree Generation: A Small Vision-Language Model for Robot Task Planning](https://mlsanity.com/paper/2603.06084/) (2026)
- [ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents](https://mlsanity.com/paper/2604.13097/) (2026)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [Towards Humanoid Robot Autonomy: A Dynamic Architecture Integrating Continuous thought Machines (CTM) and Model Context Protocol (MCP)](https://mlsanity.com/paper/2505.19339/) (2025)
- [LLM-Driven Self-Refinement for Embodied Drone Task Planning](https://mlsanity.com/paper/2508.15501/) (2025)
- [SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms](https://mlsanity.com/paper/2409.04230/) (2024)
- [Enhancing Explainability with Multimodal Context Representations for Smarter Robots](https://mlsanity.com/paper/2503.16467/) (2025)
- [Towards An LLM-Driven Unified Conversion Framework for BT and FSM in Autonomous Intelligent Systems](https://mlsanity.com/paper/2609.29228/) (2026)
- [APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction](https://mlsanity.com/paper/2610.18523/) (2026)

## Abstract (arXiv)

Autonomous robots combine skills to form increasingly complex behaviors, called missions. While skills are often programmed at a relatively low abstraction level, their coordination is architecturally separated and often expressed in higher-level languages or frameworks. State machines have been the go-to language to model behavior for decades, but recently, behavior trees have gained attention among roboticists. Although several implementations of behavior trees are in use, little is known about their usage and scope in the real world.How do concepts offered by behavior trees relate to traditional languages, such as state machines? How are concepts in behavior trees and state machines used in actual applications? This paper is a study of the key language concepts in behavior trees as realized in domain-specific languages (DSLs), internal and external DSLs offered as libraries, and their use in open-source robotic applications supported by the Robot Operating System (ROS). We analyze behavior-tree DSLs and compare them to the standard language for behavior models in robotics:state machines. We identify DSLs for both behavior-modeling languages, and we analyze five in-depth.We mine open-source repositories for robotic applications that use the analyzed DSLs and analyze their usage. We identify similarities between behavior trees and state machines in terms of language design and the concepts offered to accommodate the needs of the robotics domain. We observed that the usage of behavior-tree DSLs in open-source projects is increasing rapidly. We observed similar usage patterns at model structure and at code reuse in the behavior-tree and state-machine models within the mined open-source projects. We contribute all extracted models as a dataset, hoping to inspire the community to use and further develop behavior trees, associated tools, and analysis techniques.