Skip to main content

Thank you for visiting 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.

Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms


Optimization-based design is an effective and promising approach to realizing collective behaviours for robot swarms. Unfortunately, the domain literature often remains vague about the exact role played by the human designer, if any. It is our contention that two cases should be disentangled: semi-automatic design, in which a human designer operates and steers an optimization process (for example, by fine-tuning the parameters of the optimization algorithm); and (fully) automatic design, in which the optimization process does not involve, need or allow any human intervention. In this Perspective, we briefly review the relevant literature; illustrate the hypotheses, characteristics and core challenges of semi-automatic and automatic design; and sketch out the context in which they could be ideally applied.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Flowcharts of typical approaches to optimization-based design.


  1. 1.

    Dorigo, M., Birattari, M. & Brambilla, M. Swarm robotics. Scholarpedia 9, 1463 (2014).

    Article  Google Scholar 

  2. 2.

    Yang, G.-Z. et al. The grand challenges of Science Robotics. Sci. Robot. 3, eaar7650 (2018).

    Article  Google Scholar 

  3. 3.

    Rubenstein, M., Cornejo, A. & Nagpal, R. Programmable self-assembly in a thousand-robot swarm. Science 345, 795–799 (2014).

    Article  Google Scholar 

  4. 4.

    Werfel, J., Petersen, K. & Nagpal, R. Designing collective behavior in a termite-inspired robot construction team. Science 343, 754–758 (2014).

    Article  Google Scholar 

  5. 5.

    Garattoni, L. & Birattari, M. Autonomous task sequencing in a robot swarm. Sci. Robot. 3, eaat0430 (2018).

    Article  Google Scholar 

  6. 6.

    Slavkov, I. et al. Morphogenesis in robot swarms. Sci. Robot. 3, eaau9178 (2018).

    Article  Google Scholar 

  7. 7.

    Yu, J., Wang, B., Du, X., Wang, Q. & Zhang, L. Ultra-extensible ribbon-like magnetic microswarm. Nat. Commun. 9, 3260 (2018).

    Article  Google Scholar 

  8. 8.

    Li, S. et al. Particle robotics based on statistical mechanics of loosely coupled components. Nature 567, 361–365 (2019).

    Article  Google Scholar 

  9. 9.

    Xie, H. et al. Reconfigurable magnetic microrobot swarm: multimode transformation, locomotion, and manipulation. Sci. Robot. 4, eaav8006 (2019).

    Article  Google Scholar 

  10. 10.

    Brambilla, M., Ferrante, E., Birattari, M. & Dorigo, M. Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7, 1–41 (2013).

    Article  Google Scholar 

  11. 11.

    Brugali, D. (ed.) Software Engineering for Experimental Robotics (Springer, 2007).

  12. 12.

    Di Ruscio, D., Malavolta, I. & Pelliccione, P. A family of domain-specific languages for specifying civilian missions of multi-robot systems. In Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering Vol. 1319 (eds Aßmann, U. & Wagner, G.) 13–26 (CEUR-WS, 2014).

  13. 13.

    Bozhinoski, D., Di Ruscio, D., Malavolta, I., Pelliccione, P. & Tivoli, M. Flyaq: enabling non-expert users to specify and generate missions of autonomous multicopters. In Proceedings of the 2015 30th IEEE/ACM International Conference on Automated Software Engineering (eds Cohen, M., Grunske, L. & Whalen, M.) 801–806 (IEEE, 2015).

  14. 14.

    Schlegel, C. et al. Model-driven software systems engineering in robotics: covering the complete life-cycle of a robot. Inform. Technol. 57, 85–98 (2015).

    Google Scholar 

  15. 15.

    Hamann, H. & Wörn, H. A framework of space–time continuous models for algorithm design in swarm robotics. Swarm Intell. 2, 209–239 (2008).

    Article  Google Scholar 

  16. 16.

    Kazadi, S. Model independence in swarm robotics. Int. J. Intell. Comput. Cybern. 2, 672–694 (2009).

    MathSciNet  Article  Google Scholar 

  17. 17.

    Berman, S., Kumar, V. & Nagpal, R. Design of control policies for spatially inhomogeneous robot swarms with application to commercial pollination. In IEEE International Conference on Robotics and Automation 378–385 (IEEE, 2011).

  18. 18.

    Beal, J., Dulman, S., Usbeck, K., Viroli, M. & Correll, N. in Formal and Practical Aspects of Domain-Specific Languages: Recent Developments (ed. Mernik, M.) 436–501 (IGI Global, 2012).

  19. 19.

    Brambilla, M., Brutschy, A., Dorigo, M. & Birattari, M. Property-driven design for swarm robotics: a design method based on prescriptive modeling and model checking. ACM Trans. Auton. Adapt. Sys. 9, 17 (2014).

    Google Scholar 

  20. 20.

    Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M. & Trianni, V. A design pattern for decentralised decision making. PLoS ONE 10, e0140950 (2015).

    Article  Google Scholar 

  21. 21.

    Lopes, Y. K., Trenkwalder, S. M., Leal, A. B., Dodd, T. J. & Groβ, R. Supervisory control theory applied to swarm robotics. Swarm Intell. 10, 65–97 (2016).

    Article  Google Scholar 

  22. 22.

    Pinciroli, C. & Beltrame, G. Buzz: a programming language for robot swarms. IEEE Softw. 33, 97–100 (2016).

    Article  Google Scholar 

  23. 23.

    Hamann, H. Swarm Robotics: A Formal Approach (Springer, 2018).

  24. 24.

    Trianni, V. Evolutionary Swarm Robotics (Springer, 2008).

  25. 25.

    Hecker, J. P., Letendre, K., Stolleis, K., Washington, D. & Moses, M. E. Formica ex machina: ant swarm foraging from physical to virtual and back again. In International Conference on Swarm Intelligence Vol. 7461 (eds Dorigo, M. et al.) 252–259 (Springer, 2012).

  26. 26.

    Francesca, G. & Birattari, M. Automatic design of robot swarms: achievements and challenges. Front. Robot. AI 3, 29 (2016).

    Article  Google Scholar 

  27. 27.

    Bredeche, N., Haasdijk, E. & Prieto, A. Embodied evolution in collective robotics: a review. Front. Robot. AI 5, 12 (2018).

    Article  Google Scholar 

  28. 28.

    Brooks, R. A. Artificial life and real robots. In Towards a Practice of Autonomous Systems: Proceedings of the First European Conference on Artificial Life (eds Varela, F. J. & Bourgine, P.) 3–10 (MIT Press, 1992).

  29. 29.

    Jakobi, N., Husbands, P. & Harvey, I. Noise and the reality gap: the use of simulation in evolutionary robotics. In Advances in Artificial Life: Third European Conference on Artificial Life Vol. 929 (eds Moraán, F. et al.) 704–720 (Springer, 1995).

  30. 30.

    Nolfi, S. & Floreano, D. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines (MIT Press, 2000).

  31. 31.

    Floreano, D., Husbands, P. & Nolfi, S. in Springer Handbook of Robotics (eds Siciliano, B. & Khatib, O.) 1423–1451 (Springer, 2008).

  32. 32.

    Christensen, A. L. & Dorigo, M. Evolving an integrated phototaxis and hole-avoidance behavior for a swarm-bot. In Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems (eds Rocha, L. M. et al.) 248–254 (MIT Press, 2006).

  33. 33.

    Divband Soorati, M. & Hamann, H. The effect of fitness function design on performance in evolutionary robotics: the influence of a priori knowledge. In GECCO 2015: Proceedings of the Genetic and Evolutionary Computation Conference (ed. Silva, S.) 153–160 (ACM, 2015).

  34. 34.

    Floreano, D. & Urzelai, J. Evolutionary robots with on-line self-organization and behavioral fitness. Neural Netw. 13, 431–443 (2000).

    Article  Google Scholar 

  35. 35.

    Doncieux, S. & Mouret, J.-B. Beyond black-box optimization: a review of selective pressures for evolutionary robotics. Evol. Intell. 7, 71–93 (2014).

    Article  Google Scholar 

  36. 36.

    Silva, F., Duarte, M., Correia, L., Oliveira, S. M. & Christensen, A. L. Open issues in evolutionary robotics. Evol. Comput. 24, 205–236 (2016).

    Article  Google Scholar 

  37. 37.

    Quinn, M., Smith, L., Mayley, G. & Husbands, P. Evolving controllers for a homogeneous system of physical robots: structured cooperation with minimal sensors. Philos. Trans. R. Soc. A 361, 2321–2343 (2003).

    MathSciNet  Article  Google Scholar 

  38. 38.

    Dorigo, M. et al. Evolving self-organizing behaviors for a swarm-bot. Auton. Robots 17, 223–245 (2003).

    Article  Google Scholar 

  39. 39.

    Ampatzis, C., Tuci, E., Trianni, V. & Dorigo, M. Evolving communicating agents that integrate information over time: a real robot experiment. In Artificial Evolution: Seventh International Conference, Evolution Artificielle Vol. 3871 (eds Talbi, E. G. et al.) 248–254 (Springer, 2006).

  40. 40.

    Trianni, V. & Dorigo, M. Self-organisation and communication in groups of simulated and physical robots. Biol. Cybern. 95, 213–231 (2006).

    Article  Google Scholar 

  41. 41.

    Ampatzis, C., Tuci, E., Trianni, V., Christensen, A. L. & Dorigo, M. Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots. Artif. Life 15, 465–484 (2009).

    Article  Google Scholar 

  42. 42.

    Duarte, M. et al. Evolution of collective behaviors for a real swarm of aquatic surface robots. PLoS ONE 11, e0151834 (2016).

    Article  Google Scholar 

  43. 43.

    Jones, S., Studley, M., Hauert, S. & Winfield, A. Evolving behaviour trees for swarm robotics. In Distributed Autonomous Robotic Systems Vol. 6 (eds Groß, R. et al.) 487–501 (Springer, 2016).

  44. 44.

    Watson, R. A., Ficici, S. G. & Pollack, J. B. Embodied evolution: distributing an evolutionary algorithm in a population of robots. Robot. Auton. Syst. 39, 1–18 (2002).

    Article  Google Scholar 

  45. 45.

    Bredeche, N., Montanier, J.-M., Liu, W. & Winfield, A. Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents. Math. Comput. Model. Dyn. Syst. 18, 101–129 (2012).

    Article  Google Scholar 

  46. 46.

    Jones, S., Winfield, A., Hauert, S. & Studley, M. Onboard evolution of understandable swarm behaviors. Adv. Intell. Syst. 1, 1900031 (2019).

    Article  Google Scholar 

  47. 47.

    Birattari, M. et al. Automatic off-line design of robot swarms: a manifesto. Front. Robot. AI 6, 59 (2019).

    Article  Google Scholar 

  48. 48.

    Waibel, M., Keller, L. & Floreano, D. Genetic team composition and level of selection in the evolution of multi-agent systems. IEEE Trans. Evol. Comput. 13, 648–660 (2009).

    Article  Google Scholar 

  49. 49.

    Francesca, G., Brambilla, M., Brutschy, A., Trianni, V. & Birattari, M. AutoMoDe: a novel approach to the automatic design of control software for robot swarms. Swarm Intell. 8, 89–112 (2014).

    Article  Google Scholar 

  50. 50.

    Francesca, G. et al. An experiment in automatic design of robot swarms: AutoMoDe-Vanilla, EvoStick, and human experts. In Swarm Intelligence: 9th International Conference (eds Dorigo, M. et al.) 25–37 (Springer, 2014).

  51. 51.

    Francesca, G. et al. AutoMoDe-Chocolate: automatic design of control software for robot swarms. Swarm Intell. 9, 125–152 (2015).

    Article  Google Scholar 

  52. 52.

    Hasselmann, K., Robert, F. & Birattari, M. Automatic design of communication-based behaviors for robot swarms. In Swarm Intelligence – ANTS (eds Dorigo, M. et al.) 11172 (Springer, Cham, Switzerland, 2018), 16–29

  53. 53.

    Kuckling, J., Ligot, A., Bozhinoski, D. & Birattari, M. Behavior trees as a control architecture in the automatic modular design of robot swarms. In Swarm Intelligence: 11th International Conference (eds Dorigo, M. et al.) 30–43 (Springer, 2018).

  54. 54.

    Baldassarre, G. et al. Self-organized coordinated motion in groups of physically connected robots. IEEE Trans. Syst. Man Cybern. B 37, 224–239 (2007).

    Article  Google Scholar 

  55. 55.

    Trianni, V. & Nolfi, S. Self-organizing sync in a robotic swarm: a dynamical system view. IEEE Trans. Evol. Comput. 13, 722–741 (2009).

    Article  Google Scholar 

  56. 56.

    Gauci, M., Chen, J., Li, W., Dodd, T. J. & Groß, R. Clustering objects with robots that do not compute. In Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems 421–428 (International Foundation for Autonomous Agents and Multiagent Systems, 2014).

  57. 57.

    Gauci, M., Chen, J., Li, W., Dodd, T. J. & Groß, R. Self-organized aggregation without computation. Int. J. Robot. Res. 33, 1145–1161 (2014).

    Article  Google Scholar 

  58. 58.

    Usui, Y. & Arita, T. Situated and embodied evolution in collective evolutionary robotics. In Proceedings of the 8th International Symposium on Artificial Life and Robotics 212–215 (AROB, 2003).

Download references


The project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 681872). M.B. acknowledges support from the Belgian Fonds de la Recherche Scientifique—FNRS, of which he is a research director.

Author information




M.B. led the discussion, drafted the manuscript and coordinated its revision. All authors contributed to the elaboration of the ideas presented in the manuscript, read it and provided comments.

Corresponding author

Correspondence to Mauro Birattari.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Birattari, M., Ligot, A. & Hasselmann, K. Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. Nat Mach Intell 2, 494–499 (2020).

Download citation

Further reading


Quick links