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On the Cost of Evolving Task Specialization in Multi-Robot Systems

Paolo Leopardi, Heiko Hamann, Jonas Kuckling, Tanja Katharina Kaiser

TL;DR

This study takes first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario and shows that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists.

Abstract

Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.

On the Cost of Evolving Task Specialization in Multi-Robot Systems

TL;DR

This study takes first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario and shows that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists.

Abstract

Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.
Paper Structure (16 sections, 3 equations, 3 figures)

This paper contains 16 sections, 3 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Task, Experimental Setup, and Methods
  4. Task Description
  5. Experimental Setup
  6. Arena.
  7. Robot.
  8. Control Architecture.
  9. Evolution
  10. Post-Evaluation
  11. Results
  12. Evolution
  13. Post-Evaluation
  14. Discussion and Conclusion
  15. Acknowledgements
  16. ...and 1 more sections

Figures (3)

  • Figure 1: Overview of the foraging task: Robots have to transport objects (squares) from the source across a slope to the nest. The task can either be executed by each robot individually (generalist) or shared between robots (dropper and collector).
  • Figure 2: Best fitness (Eqs. \ref{['eq:F_G']}, \ref{['eq:F_D']}) for the generalists, droppers, and collectors over 100 generations. Dashed lines: different independent evolutionary runs; black line: mean.
  • Figure 3: Performance of groups of $2$ robots. Letters indicate the subtask performed (generalist G, dropper D, collector C), numbers indicate the evolutionary run. Each letter-number pair is associated with a robot.