Morphological Evolution of Physical Robots through Model-Free Phenotype Development

Luzius Brodbeck; Simon Hauser; Fumiya Iida

doi:10.1371/journal.pone.0128444

Morphological Evolution of Physical Robots through Model-Free Phenotype Development

PLoS One. 2015 Jun 19;10(6):e0128444. doi: 10.1371/journal.pone.0128444. eCollection 2015.

Authors

Luzius Brodbeck¹, Simon Hauser², Fumiya Iida¹

Affiliations

¹ Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland; Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom.
² Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland; Biorobotics Laboratory, EPFL-Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.

Abstract

Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Equipment Design*
Models, Theoretical
Robotics / instrumentation*

Grants and funding

This study was supported by the Swiss National Science Foundation Grant No. PP00P2123387/1 and the ETH Zurich Research Grant ETH-23-10-3. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.