Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation

2025-12-03

AI Summary

Multi-robot adaptive collaborative decision-making remains challenging in partially observable, dynamic environments. Method: This paper proposes the Interactive Inference Behavior Tree (IIBT) framework—the first to deeply integrate active inference grounded in the free-energy principle into the behavior tree (BT) architecture. IIBT employs probabilistic node modeling and an online-updatable preference matrix to enable distributed joint planning–execution closed loops, supporting multi-robot intention awareness and adaptive co-evolution of collaborative policies. While preserving BT modularity and compatibility, IIBT significantly reduces node design complexity. Contribution/Results: Experiments demonstrate that IIBT reduces node complexity by over 70% compared to conventional BTs and achieves superior robustness and environmental adaptability in maze navigation and cooperative manipulation tasks—validated in both simulation and real-robot systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference under the free energy principle for distributed multi-robot decision-making. The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots. It remains fully com- patible with standard BT architectures, allowing seamless integration into existing multi-robot control systems. Within this framework, multi-robot cooperation is formulated as a free-energy minimization process, where each robot dynamically updates its preference matrix based on perceptual inputs and peer intentions, thereby achieving adaptive coordination in partially observ- able and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative ma- nipulation task, compared against traditional BTs(https://youtu.be/KX_oT3IDTf4). Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior under environmental uncertainty.

Problem

• Integrates behavior trees with probabilistic inference for multi-robot decision-making
• Enables adaptive coordination in partially observable, dynamic environments through free-energy minimization
• Reduces node complexity while maintaining robust, interpretable cooperation under uncertainty

Innovation

• Integrates behavior trees with active inference for decision-making
• Enables online joint planning and execution across multiple robots
• Formulates cooperation as free-energy minimization for adaptive coordination