用概率推理增强行为树，让多机器人自适应协作更高效。
Bridging Probabilistic Inference and Behavior Trees: An Interactive Framework for Adaptive Multi-Robot Cooperation
- 将行为树与主动推理结合，实现在线联合规划与执行。
- 实验显示节点复杂度降低70%以上，协作更鲁棒可靠。
- 适合需要动态协调的多机器人系统，如导航与操作任务。
本文提出一种交互式推断行为树（IIBT）框架，将行为树（BTs）与自由能原理下的主动推理相结合，用于分布式多机器人决策。IIBT节点在传统行为树基础上引入概率推理能力，支持多机器人在线联合规划与执行，且完全兼容标准行为树架构，可无缝集成至现有控制系统。该框架将多机器人协作建模为自由能最小化过程，各机器人根据感知输入与同伴意图动态更新偏好矩阵，实现在部分可观测和动态环境中的自适应协调。通过仿真与真实实验验证，包括多机器人迷宫导航和协同操作任务，结果表明：相比传统行为树，IIBT将节点复杂度降低超过70%，同时在环境不确定性下仍保持稳健、可解释且自适应的协作行为。
原文摘要 · Abstract (English)
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 compatible 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 observable and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative manipulation 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.
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