Head stabilization in a humanoid robot: models and implementations

Neuroscientific studies show that humans tend to stabilize their head orientation, while accomplishing a locomotor task. This is beneficial to image stabilization and in general to keep a reference frame for the body. In robotics, too, head stabilization during robot walking provides advantages in robot vision and gaze-guided locomotion. In order to obtain the head movement behaviors found in human walk, it is necessary and sufficient to be able to control the orientation (roll, pitch and yaw) of the head in space. Based on these principles, three controllers have been designed. We developed two classic robotic controllers, an inverse kinematics based controller, an inverse kinematics differential controller and a bio-inspired adaptive controller based on feedback error learning. The controllers use the inertial feedback from a IMU sensor and control neck joints in order to align the head orientation with the global orientation reference. We present the results for the head stabilization controllers, on two sets of experiments, validating the robustness of the proposed control methods. In particular, we focus our analysis on the effectiveness of the bio-inspired adaptive controller against the classic robotic controllers. The first set of experiments, tested on a simulated robot, focused on the controllers response to a set of disturbance frequencies and a step function. The other set of experiments were carried out on the SABIAN robot, where these controllers were implemented in conjunction with a model of the vestibulo-ocular reflex (VOR) and opto-kinetic reflex (OKR). Such a setup permits to compare the performances of the considered head stabilization controllers in conditions which mimic the human stabilization mechanisms composed of the joint effect of VOR, OKR and stabilization of the head. The results show that the bio-inspired adaptive controller is more beneficial for the stabilization of the head in tasks involving a sinusoidal torso disturbance, and it shows comparable performances to the inverse kinematics controller in case of the step response and the locomotion experiments conducted on the real robot.