Active Vision for Physical Robots Using the Free Energy Principle

This paper explores the application of active inference and the free energy principle (FEP) to enable active vision in physical robots, using pixel-level RGB camera observations. By adapting existing methodologies previously limited to simulated environments, we introduce architectural improvements, including spatial softmax, to address the challenges of real-world application. Our model demonstrates proficiency in both exploratory and goal-directed behaviors within complex environments, achieving a dynamic understanding of visual scenes from pixel data. Our findings further demonstrate the potential of active inference and the FEP for tackling active vision in real-world robotics, and in bridging the gap between artificial and biological systems, offering a robust framework for developing more adaptive and aware robotic agents.