[论文解读] Active Inference for Physical AI Agents -- An Engineering Perspective

Bert de Vries|arXiv (Cornell University)|Mar 21, 2026

Embodied and Extended Cognition被引用 0

一句话总结

论文主张：以自由能原理为基础的主动推断（AIF）为物理AI代理的感知、学习、规划与控制提供统一、资源自适应的框架，通过因子图上的响应式消息传递实现。

ABSTRACT

Physical AI agents, such as robots and other embodied systems operating under tight and fluctuating resource constraints, remain far less capable than biological agents in open-ended real-world environments. This paper argues that Active Inference (AIF), grounded in the Free Energy Principle, offers a principled foundation for closing that gap. We develop this argument from first principles, following a chain from probability theory through Bayesian machine learning and variational inference to active inference and reactive message passing. From the FEP perspective, systems that maintain their structural and functional integrity over time can, under suitable assumptions, be described as minimizing variational free energy (VFE), and AIF operationalizes this by unifying perception, learning, planning, and control within a single computational objective. We show that VFE minimization is naturally realized by reactive message passing on factor graphs, where inference emerges from local, parallel computations. This realization is well matched to the constraints of physical operation, including hard deadlines, asynchronous data, fluctuating power budgets, and changing environments. Because reactive message passing is event-driven, interruptible, and locally adaptable, performance degrades gracefully under reduced resources while model structure can adjust online. We further show that, under suitable coupling and coarse-graining conditions, coupled AIF agents can be described as higher-level AIF agents, yielding a homogeneous architecture based on the same message-passing primitive across scales. Our contribution is not empirical benchmarking, but a clear theoretical and architectural case for the engineering community.

研究动机与目标

• 为物理AI代理构建一个统一、原则性框架，以结合感知、学习、规划和控制。
• 展示如何通过响应式消息传递在计算上实现变分自由能（VFE）最小化。
• 提出一个可扩展的事件驱动架构，适用于在资源波动和环境变化下工作的代理。
• 展示嵌套的主动推断代理如何在资源约束下协同运行并在线自适应。

提出的方法

• 从概率论导出通往贝叶斯机器学习和变分推断的路径，最终落到主动推断。
• 解释VFE最小化如何在Forney风格的因子图上通过响应式消息传递实现。
• 阐述受约束的变分推断（CBFE/VI）如何带来分布式推断与控制框架。
• 描述持续的响应式消息传递（RxInfer），在可变资源下实现鲁棒性。

实验结果

研究问题

• RQ1 主动推断如何为物理AI代理的感知、学习、规划与控制提供统一目标？
• RQ2 如何通过因子图上的响应式消息传递在实践中实现VFE最小化？
• RQ3 在资源约束波动的情况下，事件驱动、资源自适应架构对具身代理有哪些好处？
• RQ4 是否可以使用相同的消息传递原语将多个AIF代理耦合为更高层次的、计算同构的系统？
• RQ5 嵌套的AIF代理在产生探索性和鲁棒行为中的作用是什么？

主要发现

• VFE最小化可以通过响应式消息传递实现，从而实现分布式、并行推断。
• 统一的目标在一个计算框架内整合感知、学习、规划和控制。
• 响应式消息传递适用于硬性截止、异步数据、可变电力预算和环境变化。
• 耦合的AIF代理可以形成更高层的AIF代理，在不同尺度上使用相同的消息传递原语。
• 嵌套的AIF代理自然产生探索性行为，并在资源波动下实现鲁棒性能。