[2603.20927] Active Inference for Physical AI Agents -- An Engineering Perspective

Statistics > Machine Learning arXiv:2603.20927 (stat) [Submitted on 21 Mar 2026] Title:Active Inference for Physical AI Agents -- An Engineering Perspective Authors:Bert de Vries View a PDF of the paper titled Active Inference for Physical AI Agents -- An Engineering Perspective, by Bert de Vries View PDF 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 ...