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Multi-Level Evolution for Robotic Design.

Shelvin Chand1, David Howard1

  • 1Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD, Australia.

Frontiers in Robotics and AI
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces multi-level evolution (MLE), a novel robotic design method. MLE enhances robot design quality and scalability by layering material, component, and robot design tasks for locomotion.

Keywords:
evolutionary algorithmsevolutionary roboticsmap elitesoptimizationshape grammar

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Area of Science:

  • Robotics
  • Evolutionary Computation
  • Artificial Intelligence

Background:

  • Traditional robotic design paradigms face limitations in quality and scalability.
  • A hierarchical approach is needed to manage the complexity of robotic design.

Purpose of the Study:

  • To present a hierarchical robotic design approach based on the Multi-Level Evolution (MLE) architecture.
  • To demonstrate the effectiveness of MLE in discovering novel robot designs for locomotion tasks.

Main Methods:

  • Decomposition of the design problem into layered sub-tasks: materials, components, and robot.
  • Concurrent search for materials (properties like friction, restitution), component geometry, and morphology.
  • Integration of Quality-Diversity algorithms at each design level to foster diverse and reusable elements.

Main Results:

  • Successful application of the hierarchical MLE approach to robotic design for locomotion.
  • Discovery of a variety of reusable robotic elements (limbs, body-plans) through Quality-Diversity algorithms.
  • Generation of robot designs that are challenging to achieve with conventional methods.

Conclusions:

  • Multi-Level Evolution (MLE) offers significant advantages in robotic design quality and scalability.
  • The hierarchical decomposition and concurrent search strategy effectively addresses complex robotic design challenges.
  • MLE facilitates the discovery of innovative robotic solutions through its layered, quality-diversity-driven approach.