[2403.01977] Seeing Through Uncertainty: A Free-Energy Approach for Real-Time Perceptual Adaptation in Robust Visual Navigation

Computer Science > Robotics arXiv:2403.01977 (cs) [Submitted on 4 Mar 2024 (v1), last revised 5 Mar 2026 (this version, v3)] Title:Seeing Through Uncertainty: A Free-Energy Approach for Real-Time Perceptual Adaptation in Robust Visual Navigation Authors:Maytus Piriyajitakonkij, Rishabh Dev Yadav, Mingfei Sun, Mengmi Zhang, Wei Pan View a PDF of the paper titled Seeing Through Uncertainty: A Free-Energy Approach for Real-Time Perceptual Adaptation in Robust Visual Navigation, by Maytus Piriyajitakonkij and 4 other authors View PDF HTML (experimental) Abstract:Navigation in the natural world is a feat of adaptive inference, where biological organisms maintain goal-directed behaviour despite noisy and incomplete sensory streams. Central to this ability is the Free Energy Principle (FEP), which posits that perception is a generative process where the brain minimises Variational Free Energy (VFE) to maintain accurate internal models of the world. While Deep Neural Networks (DNNs) have served as powerful analogues for biological brains, they typically lack the real-time plasticity required to handle abrupt sensory shifts. We introduce FEP-Nav, a biologically-inspired framework that implements real-time perceptual adaptation for robust visual navigation. By decomposing VFE into its constituent components--prediction error and Bayesian surprise--we propose a dual-mechanism architecture: a Top-down Decoder that provides an internal expectation of uncorrupted sensory input, and Adap...