In this paper, we propose a novel kernel for the identification of the inverse dynamics of
robotic manipulators based on Gaussian Process Regression. The proposed kernel, called …

Abstract Machine learning approaches have recently been adopted for robot behavior
modeling and control, where robot skills are acquired and adapted from data generated by …

Accurate modeling and control of robotic systems are often critical to perform complex
manipulation tasks. Artificial Intelligence (AI) based approaches have gained widespread …

Estimating accurate forward and inverse dynamics models is a crucial component of model-
based control for sophisticated robots such as robots driven by hydraulics, artificial muscles …

This work applies machine learning to solving inverse dynamics and inverse kinematics
tasks from the motion capture data. This approach may simplify the calculation process and …

This paper provides a personal survey and retrospective of my work on robot control and
learning. It reveals an ideological agenda: Learning, and much of AI, is all about making and …

Robotics systems are now increasingly widespread in our day-life. For instance, robots have
been successfully used in several fields, like, agriculture, construction, defense, aerospace …

Abstract Recent advancements in Deep Learning have accelerated the capabilities of
robotic systems in terms of visual perception, object manipulation, automated navigation …

A revolution in Artificial Intelligent is in Progress. The name of this revolution is Deep
Learning. Since a decade, some enablers allowed its fast development. Robotic takes …

This dissertation is an outcome of a three-year doctoral program conducted at the
Department of Transdisciplinary Science and Engineering, School of Environment and …