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 …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In 1994, Karl Sims' evolved virtual creatures showed the potential of evolutionary algorithms
to produce natural, complex morphologies and behaviors [30]. One might assume that …

Evolutionary robotics is a new technqiue for the automatic creation of autonomous robots.
Inspired by the Darwinian principle of selective reproduction of the fittest, it views robots as …

The pitfalls of naive robot simulations have been recognised for areas such as evolutionary
robotics. It has been suggested that carefully validated simulations with a proper treatment of …

The reality gap, which often makes controllers evolved in simulation inefficient once
transferred onto the physical robot, remains a critical issue in evolutionary robotics (ER). We …

For several years now, various researchers have endeavored to apply artificial evolution to
the automatic design of control systems for real robots. One of the major challenges they …

The problem of the validity of simulation is particularly relevant for methodologies that use
machine learning techniques to develop control systems for autonomous robots, as, for …

In this paper we describe the evolution of a discrete-time recurrent neural network to control
a real mobile robot. In all our experiments the evolutionary procedure is carried out entirely …

Current debates regarding the possible cognitive impli-cations of ideas from adaptive
behavior research and dynamical systems theory would benefit greatly from a careful study …