Nothing Special   »   [go: up one dir, main page]
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Automatic design and manufacture of robotic lifeforms

Nature volume 406, pages 974–978 (2000)Cite this article

Abstract

Biological life is in control of its own means of reproduction, which generally involves complex, autocatalysing chemical reactions. But this autonomy of design and manufacture has not yet been realized artificially1. Robots are still laboriously designed and constructed by teams of human engineers, usually at considerable expense. Few robots are available because these costs must be absorbed through mass production, which is justified only for toys, weapons and industrial systems such as automatic teller machines. Here we report the results of a combined computational and experimental approach in which simple electromechanical systems are evolved through simulations from basic building blocks (bars, actuators and artificial neurons); the ‘fittest’ machines (defined by their locomotive ability) are then fabricated robotically using rapid manufacturing technology. We thus achieve autonomy of design and construction using evolution in a ‘limited universe’ physical simulation2,3 coupled to automatic fabrication.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Schematic illustration of an evolvable robot.
Figure 2: Phylogenetic trees of several different evolutionary runs.
Figure 3: A generation of robots.
Figure 4: Physical embodiment process.
Figure 5: Three resulting robots.

Similar content being viewed by others

References

  1. Langton, C. Artificial Life (Addison-Wesley, Redwood City, California, 1989).

    MATH  Google Scholar 

  2. Sims, K. in Proc. 4th Artificial Life Conf. (eds Brooks, R. & Maes, P.) 28–35 (MIT Press, Cambridge, MA, 1994 ).

    Google Scholar 

  3. Komosinski, M. & Ulatowski, S. in 5th Eur. Conf. on Artificial Life ECAL '99 (eds Floreano, D. et al.) 261–265 (Springer, Berlin, 1999).

  4. Smith, J. M. Byte-sized evolution. Nature 355, 772– 773 (1992).

    Article  ADS  CAS  Google Scholar 

  5. Swinson, M. Mobile Autonomous Robot Software (Report BAA-99-09, DARPA, Arlington, Virginia, 1998).

    Google Scholar 

  6. Bentley, P. (ed.) Evolutionary Design by Computers (Morgan Kaufmann, San Francisco, 1999).

    MATH  Google Scholar 

  7. Floreano, D. & Mondada, F. in From Animals to Animats III (eds Cliff, D., Husbands, P., Meyer, J. & Wilson, S.) 421– 430 (MIT Press, Cambridge, MA, 1994).

    Google Scholar 

  8. Husbands, P. & Meyer, J. A. Evolutionary Robotics (Springer, Berlin, 1998).

    Book  Google Scholar 

  9. Nolfi, S. Evolving non-trivial behaviors on real-robots: a garbage collecting robot. Robotics and Autonomous Systems, 22, 187 –198 (1992).

    Article  Google Scholar 

  10. Lund, H., Hallam, J. & Lee, W. in Proc. IEEE 3rd Int. Conf. on Evolutionary Computation (eds Fukuda, T. et al.) 384–389 (IEEE Press, Piscataway, NJ, 1996).

    Google Scholar 

  11. Thompson, A. in Evolutionary Robotics: From Intelligent Robotics to Artificial Life (ER'97) (ed. Gomi, T.) 101–125 (AAI Books, Ontario, 1997).

    Google Scholar 

  12. Funes, P. & Pollack, J. Evolutionary body building: adaptive physical designs for robots. Artif. Life 4, 337–357 (1998).

    Article  CAS  Google Scholar 

  13. Leger, C. Automated Synthesis and Optimization of Robot Configurations: An Evolutionary Approach. Thesis, Carnegie Mellon Univ. (1999).

    Google Scholar 

  14. Kochan, A. Rapid prototyping trends. Rapid Prototyp. J. 3, 150–152 (1997).

    Article  Google Scholar 

  15. Lenski, R. E., Ofria, C., Collier, T. & Adami, C. Genome complexity, robustness and genetic interactions in digital organisms. Nature 400, 661–664 ( 1999).

    Article  ADS  CAS  Google Scholar 

  16. Joy, B. Why the future doesn’t need us. WIRED Magazine 8(4) , 238–264 (2000).

    Google Scholar 

  17. Watson, R. A., Ficici, S. G. & Pollack, J. B. in '99 Congress on Evolutionary Computation (eds Angeline, P. et al.) 335–342 (IEEE Press, New York, 1999).

    Google Scholar 

  18. Beer, R. D. Intelligence as Adaptive Behavior (Academic, Boston, 1990).

    MATH  Google Scholar 

  19. Ziemelis, K. Putting it on plastic. Nature 393, 619– 620 (1998).

    Article  ADS  CAS  Google Scholar 

  20. Baughman, R. H. et al. Carbon nanotube actuators. Science 284, 1340–1344 (1999).

    Article  ADS  CAS  Google Scholar 

  21. Moravec, H. Robot—From Mere Machine to Transcendent Mind (Oxford Univ. Press, 1999).

    Google Scholar 

  22. Mahfoud, S. W. Niching Methods for Genetic Algorithms. Thesis, Univ. Illinois at Urbana-Champaign (1995).

    Google Scholar 

  23. Rechenberg, I. Evolutionsstrategie: Optimierung Technischer Systeme nach Prinzipien der Biologischen Evolution (Frommann-Holzboog, Stuttgart, 1973).

    Google Scholar 

  24. Fogel, L. J., Owens, A. J. & Walsh, M. J. Artificial Intelligence through Simulated Evolution (Wiley, New York, 1966).

    MATH  Google Scholar 

  25. Holland, J. Adaptation in Natural and Artificial Systems (Univ. Michigan Press, Ann Arbor, 1975).

    Google Scholar 

  26. Koza, J. Genetic Programming (MIT Press, Cambridge, MA, 1992 ).

    MATH  Google Scholar 

  27. Gruau, F. & Quatramaran, K. in Proc. 4th Eur. Conf. on Artificial Life (eds Husbands, P. and Harvey, I.) (MIT Press, Cambridge, MA, 1997).

    Google Scholar 

  28. Chellapilla, K. & Fogel, D. Evolution, neural networks, games and intelligence. Proc. IEEE 87, 1471–1496 (1999).

    Article  Google Scholar 

  29. Hillis, D. in Artificial Life II (eds Langton, C., Taylor, J. F. & Rasmussen, S.) 313–322 (Addison-Wesley, Reading, Massachusetts, 1992).

    Google Scholar 

  30. Pollack, J. B. & Blair, A. D. Co-evolution in the successful learning of backgammon strategy. Machine Learning 32, 225–240 ( 1998).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the DEMO Lab members for useful discussions: P. Funes, R. Watson, O. Melnik, S. Ficici, G. Hornby, E. Sklar & S. Levy. We also thank K. Quigley and G. Widberg for technical assistance. This research was partially supported by the Defense Advanced Research Projects Agency and by the Fischbach Postdoctoral Fellowship.

Author information

Authors and Affiliations

  1. Computer Science Department, Volen Center for Complex Systems, Brandeis University, Waltham, 02454, Massachusetts, USA

    Hod Lipson & Jordan B. Pollack

Authors
  1. Hod Lipson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jordan B. Pollack
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jordan B. Pollack.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lipson, H., Pollack, J. Automatic design and manufacture of robotic lifeforms. Nature 406, 974–978 (2000). https://doi.org/10.1038/35023115

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1038/35023115

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing