ArticleNature communications2021
Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Bio-Inspired Swarm Navigation on Resource-Constrained Robots for GPS-Denied Environments.Sensors (Basel, Switzerland) · 2026Article
- Towards applied swarm robotics: current limitations and enablers.Frontiers in robotics and AI · 2025Article
- Recent trends in robot learning and evolution for swarm robotics.Frontiers in robotics and AI · 2023Review
- On Using Simulation to Predict the Performance of Robot Swarms.Scientific data · 2022Article
- Social learning in swarm robotics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review.Frontiers in robotics and AI · 2021Review
- Towards an integrated automatic design process for robot swarms.Open research Europe · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.
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Registered trials
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