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Related Experiment Videos

Evolving mobile robots in simulated and real environments

O Miglino1, H H Lund, S Nolfi

  • 1Department of Psychology, University of Palermo, Italy. orazio@caio.irmkant.rm.cnr.it

Artificial Life
|January 1, 1995
PubMed
Summary

This study shows how to effectively train autonomous robots using simulations. By modeling robot dynamics and using noise, researchers can bridge the gap between simulated and real-world performance, enabling robust robotic control systems.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Simulation validity is crucial for developing control systems in autonomous robots, especially in evolutionary robotics.
  • Training robots in real environments is possible but often impractical due to the high number of trials required.

Purpose of the Study:

  • To investigate methods for improving the transfer of robot control systems from simulation to the real world.
  • To reduce the performance gap between simulated and real-world robot behaviors.
  • To validate the effectiveness of evolutionary robotics in simulated environments.

Main Methods:

  • Evolving neural controllers for a Khepera robot within computer simulations.
  • Building accurate robot-environment dynamics models by sampling real-world data.

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  • Introducing a conservative noise model to bridge the simulation-reality gap.
  • Continuing the evolutionary process in the real environment for fine-tuning.

    • Main Results:

    • An accurate model of robot-environment dynamics was successfully built.
    • A significant reduction in the performance gap between simulated and real environments was achieved using noise.
    • Continued evolution in the real environment led to robust and successful results.

    Conclusions:

    • Simulation-based training is a viable and efficient method for developing autonomous robot control systems.
    • The proposed methods effectively minimize the challenges of transferring simulated agents to physical robots.
    • Evolutionary robotics combined with simulation and real-world adaptation offers a powerful approach for robust robotic development.