# Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

arXiv: https://arxiv.org/abs/2011.09756 · HTML: https://mlsanity.com/paper/2011.09756/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 23 of 23)

- [Active Inference in Robotics and Artificial Agents: Survey and Challenges](https://mlsanity.com/paper/2112.01871/) (2021)
- [Branching Time Active Inference: the theory and its generality](https://mlsanity.com/paper/2111.11107/) (2021)
- [On efficient computation in active inference](https://mlsanity.com/paper/2307.00504/) (2023)
- [ManipDreamer: Boosting Robotic Manipulation World Model with Action Tree and Visual Guidance](https://mlsanity.com/paper/2504.16464/) (2025)
- [Branching Time Active Inference: empirical study and complexity class analysis](https://mlsanity.com/paper/2111.11276/) (2021)
- [Task and Motion Planning for Execution in the Real](https://mlsanity.com/paper/2406.03641/) (2024)
- [Deconstructing deep active inference](https://mlsanity.com/paper/2303.01618/) (2023)
- [A Study on Training and Developing Large Language Models for Behavior Tree Generation](https://mlsanity.com/paper/2401.08089/) (2024)
- [Deep Active Inference Agents for Delayed and Long-Horizon Environments](https://mlsanity.com/paper/2505.19867/) (2025)
- [Branching Time Active Inference with Bayesian Filtering](https://mlsanity.com/paper/2112.07406/) (2021)
- [Multi-Modal and Multi-Factor Branching Time Active Inference](https://mlsanity.com/paper/2206.12503/) (2022)
- [Navigating Autonomous Vehicle on Unmarked Roads with Diffusion-Based Motion Prediction and Active Inference](https://mlsanity.com/paper/2406.00211/) (2024)
- [Adaptable Recovery Behaviors in Robotics: A Behavior Trees and Motion Generators(BTMG) Approach for Failure Management](https://mlsanity.com/paper/2404.06129/) (2024)
- [Active Inference Meeting Energy-Efficient Control of Parallel and Identical Machines](https://mlsanity.com/paper/2406.09322/) (2024)
- [A Framework for Adapting Human-Robot Interaction to Diverse User Groups](https://mlsanity.com/paper/2410.11377/) (2024)
- [On Solving a Stochastic Shortest-Path Markov Decision Process as Probabilistic Inference](https://mlsanity.com/paper/2109.05866/) (2021)
- [Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots](https://mlsanity.com/paper/2310.14063/) (2023)
- [Dynamic planning in hierarchical active inference](https://mlsanity.com/paper/2402.11658/) (2024)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [Environment-Centric Active Inference](https://mlsanity.com/paper/2408.12777/) (2024)
- [Active Inference for an Intelligent Agent in Autonomous Reconnaissance Missions](https://mlsanity.com/paper/2510.17450/) (2025)
- [Active Inference for Micro-Gesture Recognition: EFE-Guided Temporal Sampling and Adaptive Learning](https://mlsanity.com/paper/2603.07559/) (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)

We propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, we specify the nominal behavior offline, through BTs. However, in contrast to previous approaches, we introduce a new type of leaf node to specify the desired state to be achieved rather than an action to execute. The decision of which action to execute to reach the desired state is performed online through active inference. This results in continual online planning and hierarchical deliberation. By doing so, an agent can follow a predefined offline plan while still keeping the ability to locally adapt and take autonomous decisions at runtime, respecting safety constraints. We provide proof of convergence and robustness analysis, and we validate our method in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment. The results showed improved runtime adaptability with a fraction of the hand-coded nodes compared to classical BTs.