Navigating in the natural world is an impressive feat of adaptive inference, where biological organisms maintain goal-directed behavior despite noisy and incomplete sensory inputs. Central to this ability is the Free Energy Principle (FEP), which posits that perception is a generative process where the brain minimizes Variational Free Energy (VFE) to maintain accurate internal models of the world. While Deep Neural Networks (DNNs) serve as powerful analogues for biological brains, they often lack the real-time plasticity needed to handle abrupt sensory shifts.
We introduce FEP-Nav, a biologically inspired framework for real-time perceptual adaptation in robust visual navigation. Motivated by the decomposition of VFE into prediction error and Bayesian surprise, FEP-Nav combines a Top-down Decoder, which provides an internal expectation of uncorrupted sensory input, with Adaptive Normalisation, which adjusts shifted feature distributions toward prior statistics. We interpret reconstruction and normalisation as approximate mechanisms for reducing the corresponding VFE-related terms during inference without gradient-based updates.
Experiments across simulated and real-world visual corruptions show that FEP-Nav restores performance lost under visual corruption, outperforming non-adaptive baselines and strong adaptive methods. These results suggest that variational principles can provide a useful design perspective for robust autonomous behavior under degraded sensory conditions.
Blogger's Review: The FEP-Nav framework showcases the potential of biologically inspired algorithms in complex environments, particularly in real-time adaptation to sensory disturbances. By combining prediction error and Bayesian surprise, FEP-Nav offers a fresh perspective on the adaptability of deep learning systems, warranting further exploration in future research. It may provide significant insights for the design of autonomous systems.