Evolving Monolithic Robot Controllers through Incremental Shaping

Joshua Evan Auerbach
Springer Berlin Heidelberg, 2011
Chapitre de livre

Evolutionary robotics has been shown to be an effective technique for generating robot behaviors that are difficult to derive analytically from the robot’s mechanics and task environment. Moreover, augmenting evolutionary algorithms with environmental scaffolding via an incremental shaping method makes it possible to evolve controllers for complex tasks that would otherwise be infeasible. In this paper we present a summary of two recent publications in the evolutionary robotics literature demonstrating how these methods can be used to evolve robot controllers for non-trivial tasks, what the obstacles are in evolving controllers in this way, and present a novel research question that can be investigated under this framework.

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Joshua Evan Auerbach
Springer Berlin Heidelberg, 2011
Chapitre de livre

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https://infoscience.epfl.ch/record/191271?ln=fr

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Concepts associés (10)

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 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

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

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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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On the relationship between environmental and morphological complexity in evolved robots

Joshua Evan Auerbach

The principles of embodied cognition dictate that intelligent behavior must arise out of the coupled dynamics of an agent's brain, body, and environment. While the relationship between controllers and morphologies (brains and bodies) has been investigated, little is known about the interplay between morphological complexity and the complexity of a given task environment. It is hypothesized that the morphological complexity of a robot should increase commensurately with the complexity of its task environment. Here this hypothesis is tested by evolving robot morphologies in a simple environment and in more complex environments. More complex robots tend to evolve in the more complex environments lending support to this hypothesis. This suggests that gradually increasing the complexity of task environments may provide a principled approach to evolving more complex robots.

ACM Press2012

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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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