Gaze Stabilization for Humanoid Robots: a Comprehensive Framework
This work addresses a specific challenge in robotics for improving visual stability in humanoid robots, but it is incremental as it builds on prior methods by adding knowledge of robot movement.
The authors tackled the problem of gaze stabilization for humanoid robots by compensating for disturbances from self-generated movements, using a framework that combines anticipatory velocity commands and gyroscope feedback, resulting in consistent reduction of residual optical flow during robot movement and external disturbances.
Gaze stabilization is an important requisite for humanoid robots. Previous work on this topic has focused on the integration of inertial and visual information. Little attention has been given to a third component, which is the knowledge that the robot has about its own movement. In this work we propose a comprehensive framework for gaze stabilization in a humanoid robot. We focus on the problem of compensating for disturbances induced in the cameras due to self-generated movements of the robot. In this work we employ two separate signals for stabilization: (1) an anticipatory term obtained from the velocity commands sent to the joints while the robot moves autonomously; (2) a feedback term from the on board gyroscope, which compensates unpredicted external disturbances. We first provide the mathematical formulation to derive the forward and the differential kinematics of the fixation point of the stereo system. We finally test our method on the iCub robot. We show that the stabilization consistently reduces the residual optical flow during the movement of the robot and in presence of external disturbances. We also demonstrate that proper integration of the neck DoF is crucial to achieve correct stabilization.