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Related Experiment Video

Updated: Apr 19, 2026

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odNEAT: An Algorithm for Decentralised Online Evolution of Robotic Controllers.

Fernando Silva1, Paulo Urbano2, Luís Correia3

  • 1Bio-inspired Computation and Intelligent Machines Lab, 1649-026 Lisboa, Portugal Instituto de Telecomunicações, 1049-001 Lisboa, Portugal; BioISI, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal fsilva@di.fc.ul.pt.

Evolutionary Computation
|December 6, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces odNEAT, a novel neuroevolution algorithm enabling robots to learn and adapt online by evolving both neural network weights and topology. odNEAT demonstrates robust performance and superior generalization in multi-robot tasks, even with system faults.

Keywords:
Artificial neural networksdecentralised algorithmsmultirobot systemsneurocontrolleronline evolution

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Evolutionary Computation

Background:

  • Online evolution allows robots to learn and adapt to dynamic environments.
  • Existing methods often fix neural network topology, limiting adaptability.
  • Decentralized learning is crucial for multi-robot systems.

Purpose of the Study:

  • Introduce odNEAT, a distributed, decentralized neuroevolution algorithm for online robot learning.
  • Evaluate odNEAT's performance in multi-robot tasks like aggregation, navigation, and phototaxis.
  • Compare odNEAT against centralized (rtNEAT) and other decentralized (IM-(μ + 1)) algorithms.

Main Methods:

  • Developed odNEAT, a neuroevolutionary algorithm that evolves both neural network weights and topology in a distributed manner.
  • Tested odNEAT on three distinct multi-robot tasks: aggregation, integrated navigation with obstacle avoidance, and phototaxis.
  • Conducted ablation studies to analyze the contribution of individual algorithmic components.

Main Results:

  • odNEAT achieved performance comparable to the centralized rtNEAT and surpassed the decentralized IM-(μ + 1) algorithm.
  • Robots using odNEAT developed less complex neural controllers with enhanced generalization capabilities.
  • The odNEAT system demonstrated high fault tolerance, enabling robots to adapt and learn new behaviors despite faults.

Conclusions:

  • odNEAT offers an effective approach for online learning and adaptation in decentralized multi-robot systems.
  • The algorithm's ability to evolve network topology contributes to superior generalization and fault tolerance.
  • Further analysis confirmed the impact of each component on odNEAT's overall performance.