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Robots evolve with a developmental process, mimicking biological growth over generations. This inverted hierarchy produces superior gaits and prevents premature evolutionary convergence in artificial agents.

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Area of Science:

  • Robotics
  • Evolutionary Computation
  • Developmental Systems

Background:

  • Evolution and development typically operate on different timescales, with evolution (generations) overarching development (lifetime).
  • This temporal hierarchy is a fundamental aspect of multicellular life but is a design choice in artificial systems like robots.

Purpose of the Study:

  • To investigate the inverse temporal hierarchy of evolution and development in robot design.
  • To explore how a developmental process integrated into evolutionary search can enhance robot morphology and function.

Main Methods:

  • Implemented a developmental process on robot morphologies across generations during an evolutionary search for gaits.
  • Robots' morphologies developed from 'baby' to 'adult' forms over successive generations.
  • Validated the approach on 2D tentacle robots and 3D voxel lattice robots, comparing it to existing methods.

Main Results:

  • The developmental approach yielded better and qualitatively different gaits compared to using only adult robots.
  • The method successfully prevented premature convergence by fostering greater exploration.
  • The approach proved effective across different robot populations and was intrinsic to morphology, not task-dependent.

Conclusions:

  • Inverting the evolutionary and developmental timescale hierarchy can lead to improved robot performance and exploration.
  • This evolutionary developmental approach offers a simple yet effective method for designing complex robotic systems.
  • Recasting evolutionary search as developmental learning opens new avenues in robotics research.