Integrating feedback and predictive control in a Bio-inspired model of visual pursuit implemented on a humanoid robot

Abstract

In order to follow a moving visual target, humans generate voluntary smooth pursuit eye movements. The purpose of smooth pursuit eye movements is to minimize the retinal slip, i.e. the target velocity projected onto the retina. In this paper we propose a model able to integrate the major characteristics of visually guided and predictive control of the smooth pursuit. The model is composed of an Inverse Dynamics Controller (IDC) for the feedback control, a neural predictor for the anticipation of the target motion and a Weighted Sum module that is able to combine the previous systems in a proper way. In order to validate the general model, two implementations with two different IDC controllers have been carried out. The first one uses a backstepping-based controller to generate velocity motor commands for the eye movements and the other one uses a bio-inspired neurocontroller to generate position motor commands for eye-neck coordinated movements. Our results, tested on the iCub robot simulator, show that both implementations can use prediction for a zero-lag visual tracking, a feedback based control for “unpredictable” target pursuit and can combine these two approaches by properly switching from one to the other, guaranteeing a stable visual pursuit.