Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

  • Frank Veenstra
  • Andres Faina
  • Sebastian Risi
  • Kasper Stoy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Modular robots offer an important benefit in evolutionary robotics, which is to quickly evaluate evolved morphologies and control systems in reality. However, artificial evolution of simulated modular robotics is a difficult and time consuming task requiring significant computational power. While artificial evolution in virtual creatures has made use of powerful generative encodings, here we investigate how a generative encoding and direct encoding compare for the evolution of locomotion in modular robots when the number of robotic modules changes. Simulating less modules would decrease the size of the genome of a direct encoding while the size of the genome of the implemented generative encoding stays the same. We found that the generative encoding is significantly more efficient in creating robot phenotypes in the initial stages of evolution when simulating a maximum of 5, 10, and 20 modules. This not only confirms that generative encodings lead to decent performance more quickly, but also that when simulating just a few modules a generative encoding is more powerful than a direct encoding for creating robotic structures. Over longer evolutionary time, the difference between the encodings no longer becomes statistically significant. This leads us to speculate that a combined approach – starting with a generative encoding and later implementing a direct encoding – can lead to more efficient evolved designs.

Keywords

Modular robots Evolutionary algorithms Direct and generative encodings 

Notes

Acknowledgement

This project was in part funded by Project ‘flora robotica’ which has received funding from the European Unions Horizon 2020 research and innovation program under the FET grant agreement, no. 640959. Computation/simulation for the work described in this paper was supported by the DeIC National HPC Centre, SDU. Special thanks to Rodrigo Moreno Garca (Universidad Nacional de Colombia) and Ceyue Liu (China University of Mining & Technology) that helped shape the design and implementation of the robotic Modules.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Frank Veenstra
    • 1
  • Andres Faina
    • 1
  • Sebastian Risi
    • 1
  • Kasper Stoy
    • 1
  1. 1.IT University of CopenhagenCopenhagenDenmark

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