Artificial Life and Robotics

, Volume 18, Issue 3–4, pp 152–160 | Cite as

Evolutionary design of soft-bodied animats with decentralized control

  • Michał Joachimczak
  • Taras Kowaliw
  • René Doursat
  • Borys Wróbel
Special Feature: Original Article


We show how a biologically inspired model of multicellular development combined with a simulated evolutionary process can be used to design the morphologies and controllers of soft-bodied virtual animats. An animat’s morphology is the result of a developmental process that starts from a single cell and goes through many cell divisions, during which cells interact via simple physical rules. Every cell contains the same genome, which encodes a gene regulatory network (GRN) controlling its behavior. After the developmental stage, locomotion emerges from the coordinated activity of the GRNs across the virtual robot body. Since cells act autonomously, the behavior of the animat is generated in a truly decentralized fashion. The movement of the animat is produced by the contraction and expansion of parts of the body, caused by the cells, and is simulated using a physics engine. Our system makes possible the evolution and development of animats that can run, swim, and actively navigate toward a target in a virtual environment.


Artificial development Evolutionary robotics Virtual animats Body-brain coevolution 



This work was supported by the Japan Society for the Promotion of Science (JSPS) through the JSPS Fellowship for Foreign Researchers, the JSPS Grant-in-Aid for Scientific Research, and the Polish National Science Center (project BIOMERGE, 2011/03/B/ST6/00399). High performance computing resources were provided by the Interdisciplinary Center for Molecular and Mathematical Modeling (ICM, University of Warsaw; project G33-8) and the Tri-city Academic Computer Center (TASK).


  1. 1.
    Bongard JC, Pfeifer R (2003) Evolving complete agents using artificial ontogeny. In: Hara F, Pfeifer R (eds) Morpho-functional machines: the new species. Springer, Japan, pp 237–258CrossRefGoogle Scholar
  2. 2.
    Calisti M, Giorelli M, Levy G, Mazzolai B, Hochner B, Laschi C, Dario P (2011) An octopus-bioinspired solution to movement and manipulation for soft robots. Bioinspir Biomim 6(3):036002+Google Scholar
  3. 3.
    Cheney N, MacCurdy R, Clune J, Lipson H (2013) Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)Google Scholar
  4. 4.
    Dellaert F, Beer RD (1996) A developmental model for the evolution of complete autonomous agents. In: From Animals to Animats 4: Proceedings of the 4th International Conference on Simulation of Adaptive Behavior (SAB 1996). MIT Press, pp 393–401Google Scholar
  5. 5.
    Doursat R (2008) Organically grown architectures: creating decentralized, autonomous systems by embryomorphic engineering. In: Würtz RP (ed) Organic computing, understanding complex systems. Springer, pp 167–199Google Scholar
  6. 6.
    Doursat R (2009) Facilitating evolutionary innovation by developmental modularity and variability. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary computation, GECCO ’09. ACM, pp 683–690Google Scholar
  7. 7.
    Doursat R, Sanchez C, Dordea R, Fourquet D, Kowaliw T (2012) Embryomorphic engineering: emergent innovation through evolutionary development. In: Doursat R, Sayama H, Michel O (eds) Morphogenetic engineering: toward programmable complex systems. Springer-Verlag, pp 275–311Google Scholar
  8. 8.
    Eggenberger Hotz P (1997) Evolving morphologies of simulated 3D organisms based on differential gene expression. In: Proceedings of the 4th European Conference on Artificial Life (ECAL 1997). MIT Press, pp 205–213Google Scholar
  9. 9.
    Gabriel KR, Sokal RR (1969) A new statistical approach to geographic variation analysis. Syst Zool 18(3):259–278CrossRefGoogle Scholar
  10. 10.
    Hiller J, Lipson H (2012) Automatic design and manufacture of soft robots. IEEE Trans Robot 28(2):457–466CrossRefGoogle Scholar
  11. 11.
    Hornby G, Lohn JD, Linden DS (2010) Computer-automated evolution of an X-band antenna for NASA’s space technology 5 mission. Evol Comput 19(1):1–23CrossRefGoogle Scholar
  12. 12.
    Joachimczak M, Kowaliw T, Doursat R, Wróbel B (2012) Brainless bodies: controlling the development and behavior of multicellular animats by gene regulation and diffusive signals. In: Artificial Life XIII: Proceedings of the 13th International Conference on the Simulation and Synthesis of Living Systems. MIT Press, pp 349–356Google Scholar
  13. 13.
    Joachimczak M, Kowaliw T, Doursat R, Wróbel B (2013) Controlling development and chemotaxis of soft-bodied multicellular animats with the same gene regulatory network. In: Proceedings of the 12th European Conference on the Synthesis and Simulation of Living Systems (ECAL 2013). MIT Press, pp 454–461Google Scholar
  14. 14.
    Joachimczak M, Wróbel B (2011) Evolution of the morphology and patterning of artificial embryos: scaling the tricolour problem to the third dimension. In: Advances in artificial life. Darwin Meets von Neumann: Proceedings of the 10th European Conference on Artificial Life (ECAL 2009), LNCS, vol 5777. Springer, pp 35–43Google Scholar
  15. 15.
    Joachimczak M, Wróbel B (2012) Co-evolution of morphology and control of soft-bodied multicellular animats. In: Proceedings of the 14th International Conference on Genetic and Evolutionary Computation, GECCO ’12. ACM, pp 561–568Google Scholar
  16. 16.
    Joachimczak M, Wróbel B (2012) Evolution of robustness to damage in artificial 3-dimensional development. Biosystems 109(3):498–505CrossRefGoogle Scholar
  17. 17.
    Komosinski M, Rotaru-Varga A (2002) Comparison of different genotype encodings for simulated three-dimensional agents. Artif Life 7(4):395–418CrossRefGoogle Scholar
  18. 18.
    Koos S, Mouret JB, Doncieux S (2013) The transferability approach: crossing the reality gap in evolutionary robotics. IEEE T Evol Comput 17(1):122–145CrossRefGoogle Scholar
  19. 19.
    Kowaliw T, Grogono P, Kharma N (2004) Bluenome: a novel developmental model of artificial morphogenesis. In: Conference on Genetic and Evolutionary Computation, GECCO ’04. pp 93–104Google Scholar
  20. 20.
    Meng Y, Zhang Y, Jin Y (2011) Autonomous self-reconfiguration of modular robots by evolving a hierarchical mechanochemical model. IEEE Comput Intell Magazine 6(1):43–54CrossRefGoogle Scholar
  21. 21.
    Orki O, Ayali A, Shai O, Ben-Hanan U (2012) Modeling of caterpillar crawl using novel tensegrity structures. Bioinspir Biomim 7(4):046006+Google Scholar
  22. 22.
    Pilat ML, Ito T, Suzuki R, Arita T (2012) Evolution of virtual creature foraging in a physical environment. In: Artificial Life XIII: Proceedings of the 13th International Conference on the Simulation and Synthesis of Living Systems. MIT Press, pp 423–430Google Scholar
  23. 23.
    Rieffel J, Knox D, Smith S, Trimmer B (2013) Growing and evolving soft robots. Artif Life (to appear). doi: 10.1162/ARTL_a_00101
  24. 24.
    Schramm L, Jin Y, Sendhoff B (2011) Emerged coupling of motor control and morphological development in evolution of multi-cellular animats. In: Advances in artificial life. Darwin Meets von Neumann: Proceedings of the 10th European Conference on Artificial Life (ECAL 2009), LNCS, vol 5777. Springer, pp 27–34Google Scholar
  25. 25.
    Schramm L, Sendhoff B (2011) An animat’s cell doctrine. In: ECAL 2011: Proceedings of the 11th European Conference on the Synthesis and Simulation of Living Systems. MIT Press, pp 739–746Google Scholar
  26. 26.
    Sfakiotakis M, Tsakiris DP (2006) Simuun: a simulation environment for undulatory locomotion. Int J Model Simul 26:350–358Google Scholar
  27. 27.
    Shepherd RF, Ilievski F, Choi W, Morin SA, Stokes AA, Mazzeo AD, Chen X, Wang M, Whitesides GM (2011) Multigait soft robot. Proc Natl Acad Sci USA. 108(51):20,400–20,403CrossRefGoogle Scholar
  28. 28.
    Shimizu M, Ishiguro A (2007) A self-reconfigurable robotic system that exhibits amoebic locomotion. In: 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, pp 101–106Google Scholar
  29. 29.
    Sims K (1994) Evolving virtual creatures. In: Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’94. ACM Press, pp 15–22Google Scholar
  30. 30.
    Steltz E, Mozeika A, Rodenberg N, Brown E, Jaeger HM (2009) Jsel: jamming skin enabled locomotion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009). pp 5672–5677Google Scholar
  31. 31.
    Trivedi D, Rahn CD, Kier WM, Walker ID (2008) Soft robotics: biological inspiration, state of the art, and future research. Appl Bionics Biomech 5(3):99–117CrossRefGoogle Scholar
  32. 32.
    Umedachi T, Takeda K, Nakagaki T, Kobayashi R, Ishiguro A (2010) Fully decentralized control of a soft-bodied robot inspired by true slime mold. Biol Cybern 102(3):261–269CrossRefGoogle Scholar

Copyright information

© ISAROB 2013

Authors and Affiliations

  • Michał Joachimczak
    • 1
    • 2
  • Taras Kowaliw
    • 3
  • René Doursat
    • 3
    • 4
    • 5
  • Borys Wróbel
    • 2
    • 6
  1. 1.Graduate School of Information ScienceNagoya UniversityNagoyaJapan
  2. 2.Systems Modeling Laboratory, Institute of OceanologyPolish Academy of SciencesSopotPoland
  3. 3.Institut des Systèmes Complexes, Paris Île-de-France (ISC-PIF)CNRSParisFrance
  4. 4.School of Biomedical EngineeringDrexel UniversityPhiladelphiaUSA
  5. 5.Erasmus Mundus Masters in Complex Systems ScienceÉcole PolytechniqueParisFrance
  6. 6.Evolutionary Systems LaboratoryAdam Mickiewicz UniversityPoznańPoland

Personalised recommendations