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Hyper-Learning Algorithms for Online Evolution of Robot Controllers

Hyper-Learning Algorithms for Online Evolution of Robot Controllers Hyper-Learning Algorithms for Online Evolution of Robot Controllers FERNANDO SILVA, Bio-inspired Computation and Intelligent Machines Lab and BioISI, Faculdade de Ci ncias, Universidade de Lisboa and Instituto de Telecomunica ões LUÍS CORREIA, BioISI, Faculdade de Ci ncias, Universidade de Lisboa ANDERS LYHNE CHRISTENSEN, Bio-inspired Computation and Intelligent Machines Lab and Instituto Universit rio de Lisboa (ISCTE-IUL) and Instituto de Telecomunica ões A long-standing goal in artificial intelligence and robotics is synthesising agents that can effectively learn and adapt throughout their lifetime. One open-ended approach to behaviour learning in autonomous robots is online evolution, which is part of the evolutionary robotics field of research. In online evolution approaches, an evolutionary algorithm is executed on the robots during task execution, which enables continuous optimisation and adaptation of behaviour. Despite the potential for automatic behaviour learning, online evolution has not been widely adopted because it often requires several hours or days to synthesise solutions to a given task. In this respect, research in the field has failed to develop a prevalent algorithm able to effectively synthesise solutions to a large number of different tasks in a timely manner. Rather than focusing on a single algorithm, we argue for more general mechanisms http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Hyper-Learning Algorithms for Online Evolution of Robot Controllers

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2017 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/3092815
Publisher site
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Abstract

Hyper-Learning Algorithms for Online Evolution of Robot Controllers FERNANDO SILVA, Bio-inspired Computation and Intelligent Machines Lab and BioISI, Faculdade de Ci ncias, Universidade de Lisboa and Instituto de Telecomunica ões LUÍS CORREIA, BioISI, Faculdade de Ci ncias, Universidade de Lisboa ANDERS LYHNE CHRISTENSEN, Bio-inspired Computation and Intelligent Machines Lab and Instituto Universit rio de Lisboa (ISCTE-IUL) and Instituto de Telecomunica ões A long-standing goal in artificial intelligence and robotics is synthesising agents that can effectively learn and adapt throughout their lifetime. One open-ended approach to behaviour learning in autonomous robots is online evolution, which is part of the evolutionary robotics field of research. In online evolution approaches, an evolutionary algorithm is executed on the robots during task execution, which enables continuous optimisation and adaptation of behaviour. Despite the potential for automatic behaviour learning, online evolution has not been widely adopted because it often requires several hours or days to synthesise solutions to a given task. In this respect, research in the field has failed to develop a prevalent algorithm able to effectively synthesise solutions to a large number of different tasks in a timely manner. Rather than focusing on a single algorithm, we argue for more general mechanisms

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Oct 9, 2017

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