A comparison between two bio-inspired adaptive models of Vestibular Ocular Reflex (VOR) implemented on the iCub robot

In order to develop efficient bio-inspired sensory-motor control for humanoid robots there are different kind of approaches that can be used to simulate a specific human behavior. The purpose of this work is to compare two models of the Vestibulo-Ocular Reflex (VOR) for the image stabilization during head movements. The VOR system has adaptive properties and this behavior resides in the cerebellum. This adaptive system uses the retinal slip as error signal and compensates for the eye dynamics, the control loop latencies and the nonlinearity due to the offset between the rotational axes of the eyeballs and the head. The adaptation mechanism has been represented by the two models in one case as a Feedback Error Learning (FEL) [1] and in the other as a decorrelation model [2]. In this work it has been shown principal results about the implementation on the simulator of the iCub robot.