Evolutionary Robots with Fast Adaptive Behavior in New Environments

Dario Floreano, Joseba Urzelai
2000
Article de conférence

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.

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Dario Floreano, Joseba Urzelai
2000
Article de conférence

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https://infoscience.epfl.ch/record/63916?ln=fr

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Robot

vignette|Atlas (2013), robot androïde de Boston Dynamics vignette|Bras manipulateurs dans un laboratoire (2009) vignette|NAO (2006), robot humanoïde éducatif d'Aldebaran Robotics vignette|DER1 (2005),

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

Evolution of Adaptive Synapses: Robots with Fast Adaptive Behavior in New Environments

Dario Floreano, Joseba Urzelai

This paper is concerned with adaptation capabilities of evolved neural controllers. We propose to evolve mechanisms for parameter self-organization instead of evolving the parameters themselves. The method consists of encoding a set of local adaptation rules that synapses follow while the robot freely moves in the environment. 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 solve the task much faster and better than evolutionary standard fixed-weight controllers, that the method scales up well to large architectures, and that evolutionary adaptive controllers can adapt to environmental changes that involve new sensory characteristics (including transfer from simulation to reality and across different robotic platforms) and new spatial relationships.

2001

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RoboGen: Robot Generation through Artificial Evolution

Joshua Evan Auerbach, Deniz Aydin, Titus Cieslewski, Ludovic Daler, Pradeep Ruben Fernando, Dario Floreano, Grégoire Hilaire Marie Heitz, Przemyslaw Mariusz Kornatowski, Ilya Loshchilov, Andrea Maesani

Science instructors from a wide range of disciplines agree that hands-on laboratory components of courses are pedagogically necessary (Freedman, 1997). However, certain shortcomings of current laboratory exercises have been pointed out by several authors (Mataric, 2004; Hofstein and Lunetta, 2004). The overarching theme of these analyses is that hands-on components of courses tend to be formulaic, closed-ended, and at times outdated. To address these issues, we envision a novel platform that is not only a didactic tool but is also an experimental testbed for users to play with different ideas in evolutionary robotics (Nolfi and Floreano, 2000), neural networks, physical simulation, 3D printing, mechanical assembly, and embedded processing. Here, we introduce RoboGen™: an open-source software and hardware platform designed for the joint evolution of robot morphologies and controllers a la Sims (1994); Lipson and Pollack (2000); Bongard and Pfeifer (2003). Robo- Gen has been designed specifically to allow evolved robots to be easily manufactured via widely available desktop 3D-printers, and the use of simple, open-source, low-cost, offthe- shelf electronic components. RoboGen features an evolution engine complete with a physics simulator, as well as utilities both for generating design files of body components for 3D printing, and for compiling neural-network controllers to run on an Arduino microcontroller board.

The MIT Press2014

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Evolutionary Robotics: Coping with Environmental Change

Dario Floreano, Joseba Urzelai

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.

2000

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