Abstract Models are among the most essential tools in robotics, such as kinematics and
dynamics models of the robot's own body and controllable external objects. It is widely …

In the attempt to build adaptive and intelligent machines, roboticists have looked at
neuroscience for more than half a century as a source of inspiration for perception and …

In this paper, we present our theoretical investigations of the technique of feedback error
learning (FEL) from the viewpoint of adaptive control. We first discuss the relationship …

We conducted a literature review on sensor heads for humanoid robots. A strong case is
made on topics involved in human robot interaction. Having found that vision is the most …

This paper reviews the current state of several formal models of speech motor control, with
particular focus on the low-level control of the speech articulators. Further development of …

The cerebellum plays an essential role in adaptive motor control. Once we are able to build
a cerebellar model that runs in realtime, which means that a computer simulation of 1 s in …

A feedback-error learning (FEL) framework, which is characterized by internal dynamics
modeling and hybrid feedback–feedforward (HFF) control, provides a computational model …

The complexity of the kinematic and dynamic structure of humanoid robots make
conventional analytical approaches to control increasingly unsuitable for such systems …

The goal of our research is to investigate the interplay between oculomotor control, visual
processing, and limb control in humans and primates by exploring the computational issues …

We embed a spiking cerebellar model within an adaptive real-time (RT) control loop that is
able to operate a real robotic body (iCub) when performing different vestibulo-ocular reflex …