Department of Engineering and Architecture, University of Trieste, Italy Email: giorgia.nadizar@phd.units.it, emedvet@units.it, fapellegrino@units.it, marco.zullich@phd.units.it"/> Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway"/> Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Norway Email: olahuser@oslomet.no"/> Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Norway Email: stenic@oslomet.no"/>
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2022 Volume 37
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RESEARCH ARTICLE   Open Access    

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

  • Department of Engineering and Architecture, University of Trieste, Italy Email: giorgia.nadizar@phd.units.it, emedvet@units.it, fapellegrino@units.it, marco.zullich@phd.units.it

  • Department of Computer Science, Artificial Intelligence Lab, Oslo Metropolitan University, Norway

  • Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Norway Email: olahuser@oslomet.no

  • Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Norway Email: stenic@oslomet.no

  • The online version of this article has been updated since its original publication. A notice detailing the changes has been published at: https://doi.org/10.1017/S0269888922000017

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  • Abstract: Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.
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  • Cite this article

    Giorgia Nadizar, Eric Medvet, Hola Huse Ramstad, Stefano Nichele, Felice Andrea Pellegrino, Marco Zullich. 2022. Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots. The Knowledge Engineering Review 37(1), doi: 10.1017/S0269888921000151
    Giorgia Nadizar, Eric Medvet, Hola Huse Ramstad, Stefano Nichele, Felice Andrea Pellegrino, Marco Zullich. 2022. Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots. The Knowledge Engineering Review 37(1), doi: 10.1017/S0269888921000151
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RESEARCH ARTICLE   Open Access    

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

  • Received: 26 August 2021
  • Revised: 01 December 2021
  • Accepted: 15 December 2021
  • Published online: 03 February 2022
The Knowledge Engineering Review  37,  2022, 37(1)  |  Cite this article

Abstract: Abstract: Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.

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    Acknowledgments

    • The experimental evaluation of this work has been supported by a Google Faculty Research Award granted to E.M. and has been partially done on CINECA HPC cluster within the CINECA-University of Trieste agreement. This work was partially funded by the Norwegian Research Council (NFR) through their IKTPLUSS research and innovation action under the project Socrates (grant agreement 270961) and Young Research Talent program under the project DeepCA (grant agreement 286558). G.N. was supported by NordSTAR - Nordic Center for Sustainable and Trustworthy AI Research (OsloMet Project Code 202237-100).

    Acknowledgments

    • The author(s) declare none.

    • The online version of this article has been updated since its original publication. A notice detailing the changes has been published at: https://doi.org/10.1017/S0269888922000017

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    • © The Author(s), 2022. Published by Cambridge University Press2022Cambridge University Press
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    Giorgia Nadizar, Eric Medvet, Hola Huse Ramstad, Stefano Nichele, Felice Andrea Pellegrino, Marco Zullich. 2022. Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots. The Knowledge Engineering Review 37(1), doi: 10.1017/S0269888921000151
    Giorgia Nadizar, Eric Medvet, Hola Huse Ramstad, Stefano Nichele, Felice Andrea Pellegrino, Marco Zullich. 2022. Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots. The Knowledge Engineering Review 37(1), doi: 10.1017/S0269888921000151
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