Navigation and Exploration with Active Inference: from Biology to Industry

2025-08-10

AI Summary

Real-time robot navigation and exploration in complex, dynamic environments without pretraining remains challenging, especially under resource constraints. Method: This paper proposes a biologically inspired navigation system grounded in the Active Inference Framework (AIF), eliminating reliance on deep learning. It integrates incremental topological mapping, uncertainty-minimizing pose inference, and goal-directed action planning to enable autonomous cognitive map construction, online localization, and adaptive exploration—all in an unsupervised, training-free manner. The approach unifies cognitive modeling with Bayesian inference principles. Contribution/Results: Implemented and validated in ROS2 across 2D/3D simulations and real-world robotic platforms, the system achieves navigation efficiency and exploration coverage comparable to state-of-the-art learning-based methods, while significantly improving cross-environment generalization and real-time performance. Its interpretability, computational efficiency, and zero-shot deployability establish a novel paradigm for embodied intelligence in resource-constrained settings.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

By building and updating internal cognitive maps, animals exhibit extraordinary navigation abilities in complex, dynamic environments. Inspired by these biological mechanisms, we present a real time robotic navigation system grounded in the Active Inference Framework (AIF). Our model incrementally constructs a topological map, infers the agent's location, and plans actions by minimising expected uncertainty and fulfilling perceptual goals without any prior training. Integrated into the ROS2 ecosystem, we validate its adaptability and efficiency across both 2D and 3D environments (simulated and real world), demonstrating competitive performance with traditional and state of the art exploration approaches while offering a biologically inspired navigation approach.

Problem

• Develops a robotic navigation system using Active Inference Framework
• Enables real-time location inference and action planning without training
• Validates adaptability in 2D/3D simulated and real-world environments

Innovation

• Active Inference Framework for robotic navigation
• Incremental topological map construction
• ROS2 integrated real time system