Multimodal Gaze Stabilization of a Humanoid Robot based on Reafferences

TL;DR

This paper presents a multimodal gaze stabilization method for humanoid robots that combines inverse kinematics with reflexes and uses a neuroscience-inspired reafference principle to improve robustness against various perturbations.

Contribution

It introduces a novel multimodal stabilization approach using reafference to modulate reflexes, enhancing robustness and versatility in humanoid robot gaze stabilization.

Findings

Each stabilization mechanism outperforms others depending on perturbation type.

Combining mechanisms via reafference provides universal stabilization.

Experimental validation on Armar-III robot demonstrates effectiveness.

Abstract

Gaze stabilization is fundamental for humanoid robots. By stabilizing vision, it enhances perception of the environment and keeps points of interest in the field of view. In this contribution, a multimodal gaze stabilization combining classic inverse kinematic control with vestibulo-ocular and optokinetic reflexes is introduced. Inspired by neuroscience, it implements a forward model that can modulate the reflexes based on the reafference principle. This principle filters self-generated movements out of the reflexive feedback loop. The versatility and effectiveness of this method are experimentally validated on the Armar-III humanoid robot. It is first demonstrated that each stabilization mechanism (inverse kinematics and reflexes) performs better than the others as a function of the type of perturbation to be stabilized. Furthermore, combining these three modalities by reafference provides a universal gaze stabilizer which can handle any kind of perturbation.