Head stabilization in a humanoid robot: models and implementations

Abstract

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

Idiothetic Verticality Estimation Through Head Stabilization Strategy

“…In terms of application to robotics, the stabilization was effective even with simplified linear control, and results in dramatically improved verticality estimation. Locating IMU in a robot's head and implementing the head stabilization strategy has been recently demonstrated in several humanoid robots applications with a view to mimic human gaze stabilization behavior [22]; to improve gaze stabilization for visual tracking [23], and to efficiently coordinate the robot's body movement [23,24]. Whereas some progress has been achieved in transferring anthropomorphic behaviors to humanoid robots, substantial research and development challenges remain in particular the role of head stabilization in resolution of gravitoinertial ambiguity.…”

Modeling of the human vestibular system and integration in a simulator for the study of orientation and balance control

“…• The subsystem Semicircular canals simulates the kinematic model explained in the previous section. The components of the angular acceleration vector resulting from this subsystem, either expressed in the system R0 (consideration of orthogonal semicircular canals) or in the system R3 (consideration of non-orthogonal semicircular canals), are integrated to obtain the angular velocity vector and subsequently each component of this vector is introduced into the transfer function (2). As a result, the volume of endolymph displaced by the cupula in the three semicircular canals (posterior, anterior and lateral) is obtained.…”

“…The design of a biologically-inspired artificial VS as a sensor for controlling robot heads during motion has received a great interest in robotics to regulate and stabilize gaze (see e.g. [2,10,3,5]). It has been demonstrated that the balance of robots that are not endowed with a vestibular sensor is clearly inferior that of humans [4].…”

The Neck of Pinobo, A Low-Cost Compliant Robot

“…The results are not optimal because we need to use a better controller with at least an integration of the errors in order to reduce static errors. There are many other ways to improve the control [7], [18] or see [8] for a review. Here, however, we focus on the hardware.…”