Network-based Active Inference for Adaptive and Cost-efficient Real-World Applications: PV Panel Inspection

ICLR 2025 Conference Submission12631 Authors

Keywords: Active Inference (AIF), Free Energy Principle (FEP), Robotics, Trajectory generation, Random dynamical systems, Random attractor dynamics, Non-Equilibrium Steady State (NESS), Adaptive control, Industrial automation, Computational efficiency, Cost-efficient solutions

TL;DR: This paper introduces Network-based Active Inference (NetAIF), a game-changing framework that merges Active Inference with network dynamics, enabling adaptive real-time robotic control while dramatically reducing computational costs and time.

Abstract:

This paper introduces Network-based Active Inference (NetAIF), a novel framework that integrates random attractor dynamics and the Free Energy Principle (FEP) to improve trajectory generation and control in robotics. NetAIF optimizes the intrinsic dynamics of neural networks, enabling robots to quickly adapt to dynamic and complex real-world environments with minimal computational resources and without the need for extensive pre-training. Unlike traditional learning methods that rely on large datasets and prolonged training periods, NetAIF offers a more efficient alternative.

In real-world scenarios, such as Photovoltaic (PV) panel inspections, NetAIF demonstrates its ability to execute dynamic tasks with both high efficiency and robustness. The system excels in unpredictable environments while maintaining a low computational footprint. These capabilities make NetAIF a promising solution for industrial applications, offering cost-effective, adaptive robotic systems that can reduce operational expenses and enhance performance, particularly in sectors like energy, where adaptability and precision are crucial.

Primary Area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)

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Submission Number: 12631