Active Vision for Physical Robots Using the Free Energy Principle

Gabriel W. Haddon-Hill, Shingo Murata

Abstract: 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.

External IDs:dblp:conf/icann/HaddonHillM24