research-article

Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics

Authors:

Thomas Schmickl,

Karl CrailsheimAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 195 - 202

https://doi.org/10.1145/2001576.2001604

Published: 12 July 2011 Publication History

Abstract

The challenging scientific field of self-reconfiguring modular robotics (i.e., decentrally controlled 'super-robots' based on autonomous, interacting robot modules with variable morphologies) calls for novel paradigms of designing robot controllers. One option is the approach of evolutionary robotics. In this approach, the challenge is to achieve high evaluation numbers with the available resources which may even affect the feasibility of this approach. Simulations are usually applied at least in a preliminary stage of research to support controller design. However, even simulations are computationally expensive which gets even more burdensome once comprehensive studies and comparisons between different controller designs and approaches have to be done. Hence, a benchmark with low computational cost is needed that still contains the typical challenges of decentral control, is comparable, and easily manageable. We propose such a benchmark and report an empirical study of its characteristics including the transition from the single-robot setting to the multi-robot setting, typical local optima, and properties of adaptive walks through the fitness landscape.

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Cited By

Zhou YTurner ANeri F(2024)Local Optima Networks for Reinforcement Learning - A Case Study: Coupled Inverted Pendulum Task2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00118(865-870)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00118
Neri FTurner A(2023)A Fitness Landscape Analysis Approach for Reinforcement Learning in the Control of the Coupled Inverted Pendulum TaskApplications of Evolutionary Computation10.1007/978-3-031-30229-9_5(69-85)Online publication date: 9-Apr-2023
https://doi.org/10.1007/978-3-031-30229-9_5
Walter ABale STyrrell A(2021)Implementation of Reduced Precision Integer Epigenetic Networks in Hardware2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659878(01-09)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9659878
Show More Cited By

Index Terms

Coupled inverted pendulums: a benchmark for evolving decentral controllers in modular robotics
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 12 July 2011

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evolutionary modular robotics

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Zhou YTurner ANeri F(2024)Local Optima Networks for Reinforcement Learning - A Case Study: Coupled Inverted Pendulum Task2024 IEEE Conference on Artificial Intelligence (CAI)10.1109/CAI59869.2024.00118(865-870)Online publication date: 25-Jun-2024
https://doi.org/10.1109/CAI59869.2024.00118
Neri FTurner A(2023)A Fitness Landscape Analysis Approach for Reinforcement Learning in the Control of the Coupled Inverted Pendulum TaskApplications of Evolutionary Computation10.1007/978-3-031-30229-9_5(69-85)Online publication date: 9-Apr-2023
https://doi.org/10.1007/978-3-031-30229-9_5
Walter ABale STyrrell A(2021)Implementation of Reduced Precision Integer Epigenetic Networks in Hardware2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9659878(01-09)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9659878
Turner AWollenberg Valero K(2021)Stochasticity Improves Evolvability in Artificial Gene Regulatory NetworksAdvances in Computational Intelligence Systems10.1007/978-3-030-87094-2_8(83-94)Online publication date: 18-Nov-2021
https://doi.org/10.1007/978-3-030-87094-2_8
Turner ALacey GSchoene ADethlefs N(2018)Evolutionary Constraint in Artificial Gene Regulatory NetworksAdvances in Computational Intelligence Systems10.1007/978-3-319-97982-3_3(29-40)Online publication date: 11-Aug-2018
https://doi.org/10.1007/978-3-319-97982-3_3
Turner ACaves LStepney STyrrell ALones M(2017)Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-ModificationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2015.249714228:1(218-230)Online publication date: Jan-2017
https://doi.org/10.1109/TNNLS.2015.2497142
Silva FDuarte MCorreia LOliveira SChristensen A(2016)Open issues in evolutionary roboticsEvolutionary Computation10.1162/EVCO_a_0017224:2(205-236)Online publication date: 1-Jun-2016
https://dl.acm.org/doi/10.1162/EVCO_a_00172
Silva FCorreia LChristensen AWeiss GYolum PBordini RElkind E(2015)R-HybrIDProceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems10.5555/2772879.2773248(735-744)Online publication date: 4-May-2015
https://dl.acm.org/doi/10.5555/2772879.2773248
Turner ATrefzer MLones MTyrrell A(2015)Evolving Efficient Solutions to Complex Problems Using the Artificial Epigenetic NetworkInformation Processing in Cells and Tissues10.1007/978-3-319-23108-2_13(153-165)Online publication date: 2-Sep-2015
https://doi.org/10.1007/978-3-319-23108-2_13
Turner ALones MFuente LStepney SCaves LTyrrell A(2013)The artificial epigenetic network2013 IEEE International Conference on Evolvable Systems (ICES)10.1109/ICES.2013.6613284(66-72)Online publication date: Apr-2013
https://doi.org/10.1109/ICES.2013.6613284
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