Multimodal Gaze Stabilization of a Humanoid Robot based on Reafferences
This work addresses the problem of robust vision stabilization for humanoid robots, representing an incremental improvement by integrating existing methods with a neuroscience-inspired modulation.
The paper tackled gaze stabilization for humanoid robots by combining inverse kinematic control with vestibulo-ocular and optokinetic reflexes using a forward model based on the reafference principle, resulting in a universal stabilizer that handles any perturbation type, as validated on the Armar-III robot.
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.