We present a comprehensive tutorial review that explores the application of bio-inspired
approaches to robot control systems for grappling and manipulating a wide range of space …

The cerebellum, which is responsible for motor control and learning, has been suggested to
act as a Smith predictor for compensation of time-delays by means of internal forward …

Robotic hand-eye coordination is recognized as an important skill to deal with complex real
environments. Conventional robotic hand-eye coordination methods merely transfer …

We propose a vision-based method to achieve Pixel-Level precision saccadic movements in
an active binocular vision system (ABVS). Traditional methods for precise saccadic motion …

In this paper, a Wavelet Functional Link Neural Network (WFLNN) structure is proposed to
comprise the intelligent part of the Feedback Error Learning (FEL) scheme alongside the …

Humans learn multisensory eye-hand coordination starting from infancy without supervision.
For an example, they learn to track their hands by exploiting various sensory modalities …

The estimation of the internal model of a robotic system results from the interaction of its
morphology, sensors and actuators, with a particular environment. Model learning …

Sensory-motor learning in humans and other animals is largely an unsupervised, adaptive,
and active process. Exteroceptive and proprioceptive modalities are crucial for motor action …

Supervised machine learning techniques have proven very effective to solve the problems
arising from model learning in robotics. A significant limitation of such approaches is that …

The information used to determine the internal model of a robot system emerges from
individual interactions with the environment. Knowledge about a specific internal model can …