A bio-inspired model of visual pursuit combining feedback and predictive control for a humanoid robot

Abstract

Humans are able to track a moving visual target by generating voluntary smooth pursuit eye movements. The purpose of smooth pursuit eye movements is to minimize the target velocity projected onto the retina (retinal slip). This is not achievable by a control based on a negative feedback due to the delays in the visual information processing. In this paper we propose a model, suitable for a robotic implementation, able to integrate the main characteristics of visual feedback and predictive control of the smooth pursuit. The model is composed of an inverse dynamics controller for the feedback control, a neural predictor for the anticipation of the target motion and an Weighted Sum module that is able to combine the previous systems in a proper way. Our results, tested on a simulated eye model of our humanoid robot, show that this model can use prediction for a zero-lag visual tracking, use a feedback based control for “unpredictable” target pursuit and combine these two approaches properly switching from one to the other, depending on the target dynamics, in order to guarantee a stable visual pursuit.