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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
Jonata Tyska Carvalho1, Stefano Nolfi2
1Informatics and Statistics Department, Federal University of Santa Catarina (UFSC), Florianópolis, Brazil Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR), Rome, Italy jonata.tyska@ufsc.br.
Evolutionary Computation
|June 30, 2023
Summary
This study introduces a method to analyze how robot controller evolution is affected by morphological variations. Results show evolutionary algorithms can tolerate significant variations, improving agent robustness and performance in diverse conditions.
Area of Science:
- Robotics
- Evolutionary Computation
- Artificial Intelligence
Background:
- Evolving robot controllers requires exposure to variable conditions for robustness and to bridge the reality gap.
- Current methods lack analysis of how morphological variations impact evolutionary processes and suitable variation range selection.
Purpose of the Study:
- To introduce a method for measuring the impact of morphological variations on robot controller evolution.
- To analyze the relationship between variation amplitude, modality, and the performance/robustness of evolving agents.
Main Methods:
- Developed a method to quantify the effects of morphological variations (initial state, sensor noise) on evolutionary algorithms.
- Analyzed the performance and robustness of evolving agents under varying morphological conditions.
Main Results:
- Evolutionary algorithms demonstrate tolerance to high-impact morphological variations.
- Variations in agent actions are better tolerated than variations in initial states or environment.
- Multiple fitness evaluations do not consistently improve accuracy.
Conclusions:
- Morphological variations enhance the robustness and performance of evolved robot controllers.
- The developed method aids in understanding and selecting appropriate variation ranges for evolutionary robotics.
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