Online Structure Learning and Planning for Autonomous Robot Navigation using Active Inference

Abstract

Autonomous navigation in unfamiliar environments requires robots to simultaneously explore, localise, and plan under uncertainty, without relying on predefined maps or extensive training. We present Active Inference MAPping and Planning (AIMAPP), a framework unifying mapping, localisation, and decision-making within a single generative model, drawing on cognitive-mapping concepts from animal navigation (topological organisation, discrete spatial representations and predictive belief updating) as design inspiration. The agent builds and updates a sparse topological map online, learns state transitions dynamically, and plans actions by minimising Expected Free Energy. This allows it to balance goal-directed and exploratory behaviours. We implemented AIMAPP as a ROS-compatible system that is sensor and robot-agnostic and integrates with diverse hardware configurations. It operates in a fully self-supervised manner, is resilient to sensor failure, continues operating under odometric drift, and supports both exploration and goal-directed navigation without any pre-training. We evaluate the system in large-scale real and simulated environments against state-of-the-art planning baselines, demonstrating its adaptability to ambiguous observations, environmental changes, and sensor noise. The model offers a modular, self-supervised solution to scalable navigation in unstructured settings. AIMAPP is available at https://github.com/decide-ugent/aimapp.