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How to Blend a Robot Within a Group of Zebrafish: Achieving Social Acceptance Through Real-Time Calibration of a Multi-level Behavioural Model

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Biomimetic and Biohybrid Systems (Living Machines 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10928))

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Abstract

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.

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References

  1. rsync(1) Linux User’s Manual

    Google Scholar 

  2. Bierbach, D., Landgraf, T., Romanczuk, P., Lukas, J., Nguyen, H., Wolf, M., Krause, J.: Using a robotic fish to investigate individual differences in social responsiveness in the guppy. bioRxiv (2018). https://doi.org/10.1101/304501

  3. Bonnet, F., Binder, S., de Oliveria, M., Halloy, J., Mondada, F.: A miniature mobile robot developed to be socially integrated with species of small fish. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 747–752. IEEE (2014)

    Google Scholar 

  4. Bonnet, F., Cazenille, L., Gribovskiy, A., Halloy, J., Mondada, F.: Multi-robots control and tracking framework for bio-hybrid systems with closed-loop interaction. In: IEEE International Conference on Robotics and Automation (ICRA). IEEE (2017)

    Google Scholar 

  5. Bonnet, F., Cazenille, L., Seguret, A., Gribovskiy, A., Collignon, B., Halloy, J., Mondada, F.: Design of a modular robotic system that mimics small fish locomotion and body movements for ethological studies. Int. J. Adv. Robot. Syst. 14(3) (2017). https://doi.org/10.1177/1729881417706628

    Article  Google Scholar 

  6. Bonnet, F., Gribovskiy, A., Halloy, J., Mondada, F.: Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor. Swarm Intell. 1–18 (2018)

    Google Scholar 

  7. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–126 (2000)

    Google Scholar 

  8. Calovi, D.S., Litchinko, A., Lecheval, V., Lopez, U., Escudero, A.P., Chaté, H., Sire, C., Theraulaz, G.: Disentangling and modeling interactions in fish with burst-and-coast swimming reveal distinct alignment and attraction behaviors. PLoS Comput. Biol. 14(1), e1005933 (2018)

    Article  Google Scholar 

  9. Cazenille, L., et al.: Automated calibration of a biomimetic space-dependent model for zebrafish and robot collective behaviour in a structured environment. In: Mangan, M., et al. (eds.) Living Machines 2017. LNCS (LNAI), vol. 10384, pp. 107–118. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63537-8_10

    Chapter  Google Scholar 

  10. Cazenille, L., Collignon, B., Bonnet, F., Gribovskiy, A., Mondada, F., Bredeche, N., Halloy, J.: How mimetic should a robotic fish be to socially integrate into zebrafish groups? Bioinspiration Biomim. (2017)

    Google Scholar 

  11. Collignon, B., Séguret, A., Halloy, J.: A stochastic vision-based model inspired by zebrafish collective behaviour in heterogeneous environments. R. Soc. Open Sci. 3(1) (2016). https://doi.org/10.1098/rsos.150473

    Article  MathSciNet  Google Scholar 

  12. Collignon, B., Séguret, A., Chemtob, Y., Cazenille, L., Halloy, J.: Collective departures in zebrafish: profiling the initiators. ar**v preprint ar**v:1701.03611 (2017)

  13. Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521(7553), 503 (2015)

    Article  Google Scholar 

  14. De Margerie, E., Lumineau, S., Houdelier, C., Yris, M.R.: Influence of a mobile robot on the spatial behaviour of quail chicks. Bioinspiration Biomim. 6(3), 034001 (2011)

    Article  Google Scholar 

  15. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  16. Deza, M., Deza, E.: Dictionary of Distances. Elsevier, Amsterdam (2006)

    MATH  Google Scholar 

  17. Fortin, F.A., Rainville, F.M.D., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)

    MathSciNet  MATH  Google Scholar 

  18. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  19. Gribovskiy, A., Halloy, J., Deneubourg, J., Mondada, F.: Designing a socially integrated mobile robot for ethological research. Robot. Autonom. Syst. 103, 42–55 (2018)

    Article  Google Scholar 

  20. Griparić, K., Haus, T., Miklić, D., Polić, M., Bogdan, S.: A robotic system for researching social integration in honeybees. PLoS ONE 12(8), e0181977 (2017)

    Article  Google Scholar 

  21. Halloy, J., Sempo, G., Caprari, G., Rivault, C., Asadpour, M., Tâche, F., Said, I., Durier, V., Canonge, S., Amé, J.: Social integration of robots into groups of cockroaches to control self-organized choices. Science 318(5853), 1155–1158 (2007)

    Article  Google Scholar 

  22. Hintjens, P.: ZeroMQ: Messaging for Many Applications. O’Reilly Media Inc., Sebastopol (2013)

    Google Scholar 

  23. Jolly, L., Pittet, F., Caudal, J.P., Mouret, J.B., Houdelier, C., Lumineau, S., De Margerie, E.: Animal-to-robot social attachment: initial requisites in a gallinaceous bird. Bioinspiration Biomim. 11(1) (2016). https://doi.org/10.1088/1748-3190/11/1/016007

  24. Katzschmann, R.K., DelPreto, J., MacCurdy, R., Rus, D.: Exploration of underwater life with an acoustically controlled soft robotic fish. Sci. Robot. 3(16) (2018). http://robotics.sciencemag.org/content/3/16/eaar3449

    Article  Google Scholar 

  25. Kim, C., Ruberto, T., Phamduy, P., Porfiri, M.: Closed-loop control of zebrafish behaviour in three dimensions using a robotic stimulus. Sci. Rep. 8(1), 657 (2018)

    Article  Google Scholar 

  26. Knight, J.: Animal behaviour: when robots go wild. Nature 434(7036), 954–955 (2005)

    Article  Google Scholar 

  27. Landgraf, T., et al.: Blending in with the shoal: robotic fish swarms for investigating strategies of group formation in guppies. In: Duff, A., Lepora, N.F., Mura, A., Prescott, T.J., Verschure, P.F.M.J. (eds.) Living Machines 2014. LNCS (LNAI), vol. 8608, pp. 178–189. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09435-9_16

    Chapter  Google Scholar 

  28. Landgraf, T., Bierbach, D., Nguyen, H., Muggelberg, N., Romanczuk, P., Krause, J.: Robofish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live trinidadian guppies. Bioinspiration Biomim. 11(1) (2016). https://doi.org/10.1088/1748-3190/11/1/015001

    Article  Google Scholar 

  29. Landgraf, T., Oertel, M., Kirbach, A., Menzel, R., Rojas, R.: Imitation of the honeybee dance communication system by means of a biomimetic robot. In: Prescott, T.J., Lepora, N.F., Mura, A., Verschure, P.F.M.J. (eds.) Living Machines 2012. LNCS (LNAI), vol. 7375, pp. 132–143. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31525-1_12

    Chapter  Google Scholar 

  30. Li, W., Gauci, M., Groß, R.: Turing learning: a metric-free approach to inferring behavior and its application to swarms. Swarm Intell. 10(3), 211–243 (2016)

    Article  Google Scholar 

  31. Mondada, F., Halloy, J., Martinoli, A., Correll, N., Gribovskiy, A., Sempo, G., Siegwart, R., Deneubourg, J.: A general methodology for the control of mixed natural-artificial societies. In: Kernbach, S. (ed.) Handbook of Collective Robotics: Fundamentals and Challenges, pp. 547–585. Pan Stanford, Singapore (2013). Chapter 15

    Chapter  Google Scholar 

  32. Mouret, J., Doncieux, S.: Encouraging behavioral diversity in evolutionary robotics: an empirical study. Evol. Comput. 20(1), 91–133 (2012)

    Article  Google Scholar 

  33. Patricelli, G.: Robotics in the study of animal behavior. In: Breed, M., Moore, J. (eds.) Encyclopedia of Animal Behavior, pp. 91–99. Greenwood Press, Westport (2010)

    Chapter  Google Scholar 

  34. Séguret, A., Collignon, B., Halloy, J.: Strain differences in the collective behaviour of zebrafish (danio rerio) in heterogeneous environment. R. Soc. Open Sci. 3(10) (2016). https://doi.org/10.1098/rsos.160451

    Article  Google Scholar 

  35. Séguret, A., Collignon, B., Cazenille, L., Chemtob, Y., Halloy, J.: Loose social organisation of AB strain zebrafish groups in a two-patch environment. ar**v preprint ar**v:1701.02572 (2017)

  36. Shi, J., Tomasi, C.: Good features to track. In: Proceedings of the Computer Vision and Pattern Recognition, CVPR (1994)

    Google Scholar 

  37. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951–2959 (2012)

    Google Scholar 

  38. Stefanec, M., Szopek, M., Schmickl, T., Mills, R.: Governing the swarm: controlling a bio-hybrid society of bees & robots with computational feedback loops. In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8. IEEE (2017)

    Google Scholar 

  39. Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)

    Article  Google Scholar 

  40. Toms, C.N., Echevarria, D.J.: Back to basics: searching for a comprehensive framework for exploring individual differences in zebrafish (danio rerio) behavior. Zebrafish 11(4), 325–340 (2014)

    Article  Google Scholar 

  41. Vaughan, R., Sumpter, N., Henderson, J., Frost, A., Cameron, S.: Experiments in automatic flock control. Robot. Autonom. Syst. 31(1), 109–117 (2000)

    Article  Google Scholar 

  42. Zabala, F., Polidoro, P., Robie, A., Branson, K., Perona, P., Dickinson, M.: A simple strategy for detecting moving objects during locomotion revealed by animal-robot interactions. Current Biol. 22(14), 1344–1350 (2012)

    Article  Google Scholar 

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Acknowledgement

This work was funded by EU-ICT project ‘ASSISIbf’, no. 601074.

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Authors and Affiliations

  1. Univ Paris Diderot, LIED, UMR 8236, 75013, Paris, France

    Leo Cazenille, Yohann Chemtob & José Halloy

  2. Sorbonne Université, CNRS, ISIR, 75005, Paris, France

    Leo Cazenille & Nicolas Bredeche

  3. Robotic Systems Laboratory, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, ME B3 30, Station 9, 1015, Lausanne, Switzerland

    Frank Bonnet, Alexey Gribovskiy & Francesco Mondada

Authors
  1. Leo Cazenille
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  2. Yohann Chemtob
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  3. Frank Bonnet
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  4. Alexey Gribovskiy
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  5. Francesco Mondada
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  6. Nicolas Bredeche
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  7. José Halloy
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Corresponding author

Correspondence to Leo Cazenille .

Editor information

Editors and Affiliations

  1. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Vasiliki Vouloutsi

  2. Laboratoire Interdisciplinaire des, Université Paris Diderot, Paris Cedex 13, France

    José Halloy

  3. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Anna Mura

  4. University of Sheffield, Sheffield, United Kingdom

    Michael Mangan

  5. Bristol University, Bristol, United Kingdom

    Nathan Lepora

  6. University of Sheffield, Sheffield, United Kingdom

    Tony J. Prescott

  7. SPECS, Institute for Bioengineering of Catalonia, Barcelona, Spain

    Paul F.M.J. Verschure

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Cazenille, L. et al. (2018). How to Blend a Robot Within a Group of Zebrafish: Achieving Social Acceptance Through Real-Time Calibration of a Multi-level Behavioural Model. In: Vouloutsi , V., et al. Biomimetic and Biohybrid Systems. Living Machines 2018. Lecture Notes in Computer Science(), vol 10928. Springer, Cham. https://doi.org/10.1007/978-3-319-95972-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-95972-6_9

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