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)


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.


Modular robots Evolutionary algorithms Direct and generative encodings 



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.


  1. 1.
    Lipson, H., Pollack, J.B.: Automatic design and manufacture of robotic lifeforms. Nature 406(6799), 974–978 (2000)CrossRefGoogle Scholar
  2. 2.
    Hornby, G.S., Lipson, H., Pollack, J.B.: Generative representations for the automated design of modular physical robots. IEEE Trans. Robot. Autom. 19(4), 703–719 (2003)CrossRefGoogle Scholar
  3. 3.
    Eiben, A.E., Bredeche, N., Hoogendoorn, M., Stradner, J., Timmis, J., Tyrrell, A.M., Winfield, A.F.T.: The triangle of life: evolving robots in real-time and real-space. In: Advances in Artificial Life, ECAL 2013, pp. 1056–1063 (2013)Google Scholar
  4. 4.
    Stoy, K.: The deformatron robot: a biologically inspired homogeneous modular robot. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 2527–2531, May 2006Google Scholar
  5. 5.
    Reece, J.B., Urry, L.A., Cain, M.L., Wasserman, S.A., Minorsky, P.V., Jackson, R.B.: Campbell Biology. Pearson, Boston (2010)Google Scholar
  6. 6.
    Floreano, D., Mattiussi, C.: Bio-Inspired Artificial Intelligence. MIT Press, Cambridge (2008)Google Scholar
  7. 7.
    Marbach, D., Ijspeert, A.J.: Online optimization of modular robot locomotion. In: IEEE International Conference Mechatronics and Automation, vol. 1, pp. 248–253, July 2005Google Scholar
  8. 8.
    Faíña, A., Bellas, F., López-Peña, F., Duro, R.J.: EDHMoR: evolutionary designer of heterogeneous modular robots. Eng. Appl. Artif. Intell. 26(10), 2408–2423 (2013)CrossRefGoogle Scholar
  9. 9.
    Guettas, C., Cherif, F., Breton, T., Duthen, Y.: Cooperative co-evolution of configuration and control for modular robots. In: 2014 International Conference on Multimedia Computing and Systems, ICMCS 2014, pp. 26–31, October 2015Google Scholar
  10. 10.
    Sims, K.: Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, pp. 15–22, July 1994Google Scholar
  11. 11.
    Sims, K.: Evolving 3D morphology and behavior by competition. Artif. Life 1(4), 353–372 (1994)CrossRefGoogle Scholar
  12. 12.
    Hornby, G., Pollack, J.: The advantages of generative grammatical encodings for physical design. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No. 01TH8546), vol. 1, pp. 600–607 (2001)Google Scholar
  13. 13.
    Auerbach, J.E., Bongard, J.C.: Evolving complete robots with CPPN-NEAT: the utility of recurrent connections. In: Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 1475–1482 (2011)Google Scholar
  14. 14.
    Cheney, N., MacCurdy, R., Clune, J., Lipson, H.: Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. In: Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, p. 167 (2013)Google Scholar
  15. 15.
    Bonardi, S., Vespignani, M., Moeckel, R., Kieboom, J.V.D., Pouya, S., Sproewitz, A., Ijspeert, A.J.: Automatic generation of reduced CPG control networks for locomotion of arbitrary modular robot structures. In: Proceedings of Robotics: Science and Systems (2014)Google Scholar
  16. 16.
    Auerbach, J.E., Heitz, G., Kornatowski, P.M., Floreano, D.: Rapid evolution of robot gaits. In: GECCO 2015, pp. 743–744 (2015)Google Scholar
  17. 17.
    Lindenmayer, A.: Mathematical models for cellular interactions in development I. Filaments with one-sided inputs. J. Theor. Biol. 18(3), 280–299 (1968)CrossRefGoogle Scholar
  18. 18.
    Kamimura, A., Kurokawa, H., Yoshida, E., Murata, S., Tomita, K., Kokaji, S.: Automatic locomotion design and experiments for a modular robotic system. IEEE/ASME Trans. Mechatron. 10(3), 314–325 (2005)CrossRefGoogle Scholar
  19. 19.
    Sproewitz, A., Moeckel, R., Maye, J., Ijspeert, A.J.: Learning to move in modular robots using central pattern generators and online optimization. Int. J. Robot. Res. 27(3–4), 423–443 (2008)CrossRefGoogle Scholar
  20. 20.
    Still, S., Hepp, K., Douglas, R.J.: Neuromorphic walking gait control. IEEE Trans. Neural Netw. 17(2), 496–508 (2006)CrossRefGoogle Scholar
  21. 21.
    Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21(4), 642–653 (2008)CrossRefGoogle Scholar
  22. 22.
    Rohmer, E., Singh, S.P.N., Freese, M.: V-REP: a versatile and scalable robot simulation framework. In: IEEE International Conference on Intelligent Robots and Systems, pp. 1321–1326 (2013)Google Scholar
  23. 23.
    Faiña, A., Orjales, F., Bellas, F., Duro, R.: First steps towards a heterogeneous modular robotic architecture for for intelligent industrial operation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011 (2011)Google Scholar
  24. 24.
    Cheney, N., Bongard, J., Sunspiral, V., Lipson, H.: On the difficulty of co-optimizing morphology and control in evolved virtual creatures. In: Proceedings of the Artificial Life Conference 2016, ALIFE XV, pp. 226–234 (2016)Google Scholar
  25. 25.
    Lindenmayer, A., Jürgensen, H.: Grammars of development: discrete-state models for growth, differentiation, and gene expression in modular organisms. In: Rozenberg, G., Salomaa, A. (eds.) Lindenmayer Systems: Impacts on Theoretical Computer Science, Computer Graphics, and Developmental Biology, pp. 3–21. Springer, Heidelberg (1992)CrossRefGoogle Scholar
  26. 26.
    Veenstra, F., Faina, A., Stoy, K., Risi, S.: Generating artificial plant morphologies for function and aesthetics through evolving L-Systems. In: Proceedings of the Artificial Life Conference 2016, pp. 692–699. MIT Press (2016)Google Scholar
  27. 27.
    Veenstra, F., Faina, A., Risi, S., Stoy, K.: Video: evolving modular robots using direct and generative encodings (2017).
  28. 28.
    Cook, O.F.: Factors of species-formation. Science 23(587), 506–507 (1906)CrossRefGoogle Scholar
  29. 29.
    Stanley, K.O., Miikkulainen, R.: Efficient evolution of neural network topologies. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, pp. 1757–1762 (2002)Google Scholar
  30. 30.
    Lehman, J., Stanley, K.O.: Exploiting open-endedness to solve problems through the search for novelty. In: Artificial Life XI, pp. 329–336 (2008)Google Scholar
  31. 31.
    Hornby, G.S.: ALPS: the age-layered population structure for reducing the problem of premature convergence. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 815–822 (2006)Google Scholar
  32. 32.
    Hornby, G.S.: The age-layered population structure (ALPS) evolutionary algorithm. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (2009)Google Scholar
  33. 33.
    Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8(2), 131–162 (2007)CrossRefGoogle Scholar
  34. 34.
    Christensen, D.J., Schultz, U.P., Stoy, K.: A distributed and morphology-independent strategy for adaptive locomotion in self-reconfigurable modular robots. Robot. Auton. Syst. 61(9), 1021–1035 (2013)CrossRefGoogle Scholar
  35. 35.
    Pfeifer, R., Iida, F.: Morphological computation: connecting body, brain and environment. Japan. Sci. Mon. 58(2), 48–54 (2005)Google Scholar
  36. 36.
    Prusinkiewicz, P., Lindenmayer, A.: The algorithmic beauty of plants. Plant Sci. 122(1), 109–110 (1997). doi: 10.1016/S0168-9452(96)04526-8CrossRefzbMATHGoogle Scholar

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

Personalised recommendations