Considering the increasing importance of Artificial Intelligence in Education (AIEd) and the
absence of a comprehensive review on it, this research aims to conduct a comprehensive …

The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …

Reinforcement learning offers to robotics a framework and set of tools for the design of
sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic …

Nonlinear dynamical systems have been used in many disciplines to model complex
behaviors, including biological motor control, robotics, perception, economics, traffic …

Learning new motor tasks from physical interactions is an important goal for both robotics
and machine learning. However, when moving beyond basic skills, most monolithic machine …

Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex
behaviors through experience. However, realizing this promise for long-horizon tasks in the …

This review investigates two recent developments in artificial intelligence and neural
computation: learning from imitation and the development of humanoid robots. It is …

By now it is widely accepted that learning a task from scratch, ie, without any prior
knowledge, is a daunting undertaking. Humans, however, rarely at (cid: 173) tempt to learn …

Recent empirical studies have implicated the use of the motor system during action
observation, imitation and social interaction. In this paper, we explore the computational …