Robotic implementation of biological Bayesian models for visuo-inertial image stabilization and gaze control

Robotic implementations of gaze control and image stabilization have been previously proposed, that rely on fusing inertial and visual sensing modalities. Human and biological system also combine the two sensing modalities for the same goal. In this work we build upon these previous results and, with the contribution of psychophysical studies, attempt a more bio-inspired approach to the robotic implementation. Since Bayesian models have been successfully used to explain psychophysical experimental findings, we propose a robotic implementation using Bayesian inference.