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Robotics
Special Issues
Evolutionary Robotics
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Evolutionary Robotics

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 10776

Special Issue Editor

Prof. Dr. Stefano Nolfi

E-Mail Website
Guest Editor
Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR-ISTC), Via S. Martino della Battaglia 44, 00185 Roma, Italy
Interests: evolutionary robotics; adaptive behaviour

Special Issue Information

Dear Colleagues,

Over the last ten years or so, evolutionary robotics have attracted the interest of a large community of researchers with different research interests and backgrounds, ranging from AI and robotics, over biology and cognitive science, to the study of social behavior.

Continuous progress in evolutionary robotics has led to a substantial maturation of the field and a clearer understanding of its potential and of its current limitations. The goal of this Special Issue is to encourage researchers working in this field not only to present their most recent research, but also to discuss the relation with other related areas such as evolutionary computation, reinforcement learning, and developmental robotics.

Papers addressing (but not limited to) the following topics are welcome:

Body and brain co-evolution
Evolving soft robots
Evolutionary swarm robotics
Competitive co-evolution
Open-ended evolution
Robustness
Adaptation to faults and to dynamical environments
Behavioral and neural plasticity
Evolution and evolvability
Modularity and behavior arbitration
Evolution, development, and learning
Evolution of communication and language
Anticipation and sensory deprivation
World models
Benchmarking and good practices for evolutionary robotics

Prof. Dr. Stefano Nolfi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Autonomous robots
  • Evolutionary computation
  • Continuous control optimization
  • Neuroevolution
  • Morphological computation
  • Embodied cognition
  • Collective behavior
  • Swarm intelligence
  • Language evolution
  • Open-ended evolution
  • Self-play
  • Soft robots

Published Papers (2 papers)

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Research

14 pages, 1910 KiB  
Article
Development of Multiple Behaviors in Evolving Robots
by Victor Massagué Respall and Stefano Nolfi
Robotics 2021, 10(1), 1; https://0-doi-org.brum.beds.ac.uk/10.3390/robotics10010001 - 23 Dec 2020
Cited by 1 | Viewed by 2911
Abstract
We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first [...] Read more.
We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered. Full article
(This article belongs to the Special Issue Evolutionary Robotics)
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24 pages, 20856 KiB  
Article
Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication
by Edgar Buchanan, Léni K. Le Goff, Wei Li, Emma Hart, Agoston E. Eiben, Matteo De Carlo, Alan F. Winfield, Matthew F. Hale, Robert Woolley, Mike Angus, Jon Timmis and Andy M. Tyrrell
Robotics 2020, 9(4), 106; https://0-doi-org.brum.beds.ac.uk/10.3390/robotics9040106 - 07 Dec 2020
Cited by 13 | Viewed by 6805
Abstract
A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due [...] Read more.
A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability. Full article
(This article belongs to the Special Issue Evolutionary Robotics)
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