# Comparison between Behavior Trees and Finite State Machines

arXiv: https://arxiv.org/abs/2405.16137 · HTML: https://mlsanity.com/paper/2405.16137/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2024.

## Built on by (most cited first, 5 of 5)

- [Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms](https://mlsanity.com/paper/2511.15274/) (2025)
- [Execution Semantics of Behavior Trees in Robotic Applications](https://mlsanity.com/paper/2408.00090/) (2024)
- [Reward Shaping and Action Masking for Compositional Tasks using Behavior Trees and LLMs](https://mlsanity.com/paper/2605.05795/) (2026)
- [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)

Behavior Trees (BTs) were first conceived in the computer games industry as a tool to model agent behavior, but they received interest also in the robotics community as an alternative policy design to Finite State Machines (FSMs). The advantages of BTs over FSMs had been highlighted in many works, but there is no thorough practical comparison of the two designs. Such a comparison is particularly relevant in the robotic industry, where FSMs have been the state-of-the-art policy representation for robot control for many years. In this work we shed light on this matter by comparing how BTs and FSMs behave when controlling a robot in a mobile manipulation task. The comparison is made in terms of reactivity, modularity, readability, and design. We propose metrics for each of these properties, being aware that while some are tangible and objective, others are more subjective and implementation dependent. The practical comparison is performed in a simulation environment with validation on a real robot. We find that although the robot's behavior during task solving is independent on the policy representation, maintaining a BT rather than an FSM becomes easier as the task increases in complexity.