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Morphological Evolution of Physical Robots through Model-Free Phenotype Development.

Luzius Brodbeck1, Simon Hauser2, Fumiya Iida1

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This study introduces artificial evolution for physical systems, enabling robots to autonomously design and build new robots. This model-free approach improves robot functionality through real-world testing and iterative design.

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Area of Science:

  • Robotics
  • Evolutionary Computation
  • Artificial Intelligence

Background:

  • Artificial evolution optimizes physical systems by iteratively adapting machines to a target function.
  • Traditional methods rely on physics models, risking a reality gap between simulation and real-world performance.
  • Developing variations of physical machines is crucial for effective design optimization.

Purpose of the Study:

  • To demonstrate model-free development and evaluation of physical phenotypes in artificial evolution.
  • To enable autonomous design and assembly of locomotion agents by a mother robot.
  • To improve robot functionality through real-world feedback and iterative design.

Main Methods:

  • A mother robot autonomously designs and assembles locomotion agents.
  • Locomotion agents are evaluated in a real-world testing environment.
  • Feedback from locomotion behavior analysis informs the next design iteration.

Main Results:

  • Successfully developed and evaluated 500 autonomously built locomotion agents.
  • Demonstrated diversification in morphology and behavior of physical robots.
  • Achieved improvement in functionality using limited resources.

Conclusions:

  • Model-free artificial evolution is a viable approach for physical system design.
  • Autonomous robots can iteratively improve their own designs through real-world interaction.
  • This method offers a robust alternative to model-based optimization in robotics.