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Lamarckian Evolution of Simulated Modular Robots.

Milan Jelisavcic1, Kyrre Glette2, Evert Haasdijk1

  • 1Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.

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|January 27, 2021
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Summary
This summary is machine-generated.

This study explores evolutionary robotics, enabling robots to evolve both bodies and brains. Lamarckian inheritance of parental controllers significantly benefits infant robot learning and locomotion speed.

Keywords:
Lamarckian evolutionartificial lifeembodied evolutionevolutionary roboticsmodular robotsonline learning

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Area of Science:

  • Evolutionary robotics
  • Artificial intelligence
  • Robotics

Background:

  • Robots with evolvable bodies and brains require a post-birth learning phase.
  • Adapting controllers to new morphologies is crucial for robot development.

Purpose of the Study:

  • Investigate bootstrapping infant robot learning using Lamarckian inheritance.
  • Analyze the benefits and conditions influencing inherited parental controllers.

Main Methods:

  • Developed an evolutionary robot system with evolvable morphologies and controllers.
  • Controllers combine morphology-dependent (CPG) and independent (CPPN) components.
  • Implemented Lamarckian inheritance for the CPPN component between generations.

Main Results:

  • Inheriting optimized parental controllers enhances infant robot learning and locomotion speed.
  • Identified conditions that modulate the benefits of Lamarckian inheritance.
  • Demonstrated that evolutionary strategies for controllers impact evolved morphologies.

Conclusions:

  • Lamarckian inheritance of controllers is a viable strategy for accelerating learning in evolutionary robotics.
  • The CPPN-CPG controller architecture facilitates cross-morphology controller transfer.
  • Evolved robot systems can benefit from inheritance mechanisms that bridge developmental and evolutionary processes.