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Lexicase Selection for Multi-Task Evolutionary Robotics.

Adam Stanton1, Jared M Moore2

  • 1Aston University, School of Informatics and Digital Engineering. a.j.stanton@aston.ac.uk.

Artificial Life
|August 19, 2022
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Summary
This summary is machine-generated.

Lexicase selection can evolve multiple tasks in evolutionary robotics, but performance depends on task similarity and presentation strategy. Uniform random sampling is most efficient for multi-task learning.

Keywords:
Multi-objectiveevolutionary roboticslexicase selectionmany-objectivetransfer learning

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Area of Science:

  • Evolutionary Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Lexicase selection is effective for single tasks with many parameterizations in evolutionary robotics.
  • Generalization across task configurations is a key capability of evolved agents.
  • Investigating multi-task learning with Lexicase selection is a novel research direction.

Purpose of the Study:

  • To assess the feasibility of multi-task evolution using Lexicase selection.
  • To analyze the impact of introducing new tasks on evolutionary adaptation.
  • To explore how task presentation schedules influence evolutionary outcomes.

Main Methods:

  • A quadruped robot controlled by a feed-forward neural network was used.
  • Lexicase selection was employed for parent selection during evolutionary training.
  • Simultaneous adaptation in wall-crossing, turn-and-seek, and cargo-carry tasks was investigated, with each task having 100 variants.

Main Results:

  • Lexicase successfully integrated multi-task evolution, but with a performance penalty compared to single-task evolution.
  • Task complementarity (e.g., wall-cross/turn-and-seek) resulted in better performance than antagonistic tasks (e.g., wall-cross/cargo-carry).
  • Uniform and even sampling strategies outperformed generational sampling for antagonistic tasks, requiring fewer computational resources.

Conclusions:

  • Lexicase selection is a viable mechanism for multi-task evolution of neural controllers in robotics.
  • Task interference significantly impacts performance, highlighting the importance of task selection and scheduling.
  • Uniform random sampling is recommended for its balance of performance, simplicity, and computational efficiency in multi-task scenarios.