Evolutionary Robotics: Coping with Environmental Change

Dario Floreano, Joseba Urzelai
2000
Article de conférence

In this paper an evolutionary method consisting of encoding a set of local adaptation rules that synapses obey while a robot freely moves in the environment is compared to standard evolution of fixed-weight control networks. The results show ha evolutionary adaptive controllers can adapt online without additional evolutionary training to strong environmental changes where instead the performance of evolutionary fixed- weight controllers is significantly degraded. Two cases are described: transfer of evolved controllers from simulated to real robots and across different robotic platforms that vary in size, shape, and sensor response profile. In both cases evolved adaptive controllers autonomously and quickly adjust synaptic weights to successfully accomplish the task in the new conditions.

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Dario Floreano, Joseba Urzelai
2000
Article de conférence

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https://infoscience.epfl.ch/record/63917?ln=fr

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Evolutionary robotics

Evolutionary robotics is an embodied approach to Artificial Intelligence (AI) in which robots are automatically designed using Darwinian principles of natural selection. The design of a robot, or a su

Robotique

thumb|upright=1.5|Nao, un robot humanoïde. thumb|upright=1.5|Des robots industriels au travail dans une usine. La robotique est l'ensemble des techniques permettant la conception et la réalisation de

Algorithme évolutionniste

vignette|redresse=1.2|Un algorithme évolutionnaire utilise itérativement des opérateurs de sélections (en bleu) et de variation (en jaune). i : initialisation, f(X) : évaluation, ? : critère d'arr

Evolutionary Robotics: The Next Generation

Dario Floreano, Joseba Urzelai

After reviewing current approaches in Evolutionary Robotics, we point to directions of research that re likely to bring interesting results in the future. e then address two crucial aspects for future developments of Evolutionary Robotics: choice of fitness functions and scalability to real-world situations. In the first case we suggest framework to describe fitness functions, choose them according to the situation constraints, and compare available experiments in the literature on evolutionary robotics. In the second case, we suggest way to make experimental results applicable to real- world situations by evolving online continuous adaptive controllers. We also give an overview of recent experimental results showing that the suggested approaches pro- duce qualitatively superior abilities, scale up to more complex architectures, smoothly transfer from simulations to real robots and across different robotic platforms, and autonomously adapt in few seconds to several sources of strong variability that were not included during the evolutionary run.

AAI Books2000

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Evolutionary On-Line Self-Organization of Autonomous Robots

Dario Floreano, Joseba Urzelai

We review recent experiments in evolutionary robotics carried out in dynamic environments and across different robotic platforms. We then introduce a new evolutionary approach where robots are evolved for their ability to adapt online. Several experiments show that this new approach is much faster, more powerful, and scalable that the traditional approach.

2000

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Evolutionary Robots with Fast Adaptive Behavior in New Environments

Dario Floreano, Joseba Urzelai

This paper is concerned with adaptation capabilities of evolved neural controllers. A method consisting of encoding a set of local adaptation rules that synapses obey while the robot freely moves in the environment [6] is compared to a standard fixed-weight network. In the experiments presented here, the performance of the robot is measured in environments that are different in significant ways from those used during evolution. The results show that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfers from simulation to reality) and new spatial relationships.

2000

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