Biomimetic robots that replace living social interaction partners can help elucidate the underlying interaction rules in animal groups. Our review focuses on the use of interactive robots that respond dynamically to animal behavior as part of a closed control loop. We discuss the most influential works to date and how they have contributed to our understanding of animal sociality. Technological advances permit the use of robots that can adapt to the situations they face and the conspecifics they encounter, or robots that learn to optimize their social performance from a set of experiences. We discuss how adaptation and learning may provide novel insights into group sociobiology and describe the technical challenges associatedwith these types of interactive robots. This interdisciplinary field provides a rich set of problems to be tackled by roboticists, machine learning engineers, and control theorists. By cultivating smarter robots, we can usher in an era of more nuanced exploration of animal behavior.


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Literature Cited

  1. 1. 
    Webb B. 2000. What does robotics offer animal behaviour. ? Anim. Behav. 60:545–58
    [Google Scholar]
  2. 2. 
    Krause J, Winfield AF, Deneubourg JL 2011. Interactive robots in experimental biology. Trends Ecol. Evol. 26:369–75
    [Google Scholar]
  3. 3. 
    Romano D, Donati E, Benelli G, Stefanini C 2018. A review on animal–robot interaction: from bio-hybrid organisms to mixed societies. Biol. Cybernet. 113:201–25
    [Google Scholar]
  4. 4. 
    Datteri E. 2020. Interactive biorobotics. Synthese In press. https://doi.org/10.1007/s11229-020-02533-2
    [Crossref] [Google Scholar]
  5. 5. 
    Jolles JW, Weimar N, Landgraf T, Romanczuk P, Krause J, Bierbach D 2020. Group-level patterns emerge from individual speed as revealed by an extremely social robotic fish. Biol. Lett. 16:20200436
    [Google Scholar]
  6. 6. 
    Chouinard-Thuly L, Gierszewski S, Rosenthal GG, Reader SM, Rieucau G et al. 2017. Technical and conceptual considerations for using animated stimuli in studies of animal behavior. Curr. Zool. 63:5–19
    [Google Scholar]
  7. 7. 
    Moussaïd M, Kapadia M, Thrash T, Sumner RW, Gross M et al. 2016. Crowd behaviour during high-stress evacuations in an immersive virtual environment. J. R. Soc. Interface 13:20160414
    [Google Scholar]
  8. 8. 
    Stowers JR, Hofbauer M, Bastien R, Griessner J, Higgins P et al. 2017. Virtual reality for freely moving animals. Nat. Methods 14:995–1002
    [Google Scholar]
  9. 9. 
    Halloy J, Sempo G, Caprari G, Rivault C, Asadpour M et al. 2007. Social integration of robots into groups of cockroaches to control self-organized choices. Science 318:1155–58
    [Google Scholar]
  10. 10. 
    Bierbach D, Landgraf T, Romanczuk P, Lukas J, Nguyen H et al. 2018. Using a robotic fish to investigate individual differences in social responsiveness in the guppy. R. Soc. Open Sci. 5:181026
    [Google Scholar]
  11. 11. 
    Landgraf T, Oertel M, Rhiel D, Rojas R 2010. A biomimetic honeybee robot for the analysis of the honeybee dance communication system. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems3097–102 Piscataway, NJ: IEEE
    [Google Scholar]
  12. 12. 
    Kawabata K, Aonuma H, Hosoda K, Sugimoto Y, Xue J 2014. Experimental study on robotic interactions to the cricket. 2014 IEEE International Conference on Robotics and Biomimetics949–54 Piscataway, NJ: IEEE
    [Google Scholar]
  13. 13. 
    Kopman V, Laut J, Polverino G, Porfiri M 2013. Closed-loop control of zebrafish response using a bioinspired robotic-fish in a preference test. J. R. Soc. Interface 10:20120540
    [Google Scholar]
  14. 14. 
    Cazenille L, Chemtob Y, Bonnet F, Gribovskiy A, Mondada F 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. Biomimetic and Biohybrid Systems V Vouloutsi, J Halloy, A Mura, M Mangan, N Lepora et al.73–84 Cham, Switz: Springer
    [Google Scholar]
  15. 15. 
    Spinello C, Yang Y, Macr S, Porfiri M 2019. Zebrafish adjust their behavior in response to an interactive robotic predator. Front. Robot. AI 6:38
    [Google Scholar]
  16. 16. 
    Chemtob Y, Cazenille L, Bonnet F, Gribovskiy A, Mondada F, Halloy J 2020. Strategies to modulate zebrafish collective dynamics with a closed-loop biomimetic robotic system. Bioinspir. Biomimet. 15:046004
    [Google Scholar]
  17. 17. 
    Landgraf T, Nguyen H, Forgo S, Schneider J, Schröer J et al. 2013. Interactive robotic fish for the analysis of swarm behavior. Advances in Swarm Intelligence Y Tan, Y Shi, H Mo 1–10 Berlin: Springer
    [Google Scholar]
  18. 18. 
    Landgraf T, Nguyen H, Schröer J, Szengel A, Clément RJG et al. 2014. Blending in with the shoal: robotic fish swarms for investigating strategies of group formation in guppies. Biomimetic and Biohybrid Systems A Duff, NF Lepora, A Mura, TJ Prescott, PFMJ Verschure 178–189 Cham, Switz: Springer
    [Google Scholar]
  19. 19. 
    Landgraf T, Bierbach D, Nguyen H, Muggelberg N, Romanczuk P, Krause J 2016. RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies. Bioinspir. Biomimet. 11:015001
    [Google Scholar]
  20. 20. 
    Worm M, Landgraf T, Prume J, Nguyen H, Kirschbaum F, von der Emde G 2018. Evidence for mutual allocation of social attention through interactive signaling in a mormyrid weakly electric fish. PNAS 115:6852–57
    [Google Scholar]
  21. 21. 
    Swain DT, Couzin ID, Leonard NE 2012. Real-time feedback-controlled robotic fish for behavioral experiments with fish schools. Proc. IEEE 100:150–63
    [Google Scholar]
  22. 22. 
    Shi Q, Ishii H, Tanaka K, Sugahara Y, Takanishi A et al. 2015. Behavior modulation of rats to a robotic rat in multi-rat interaction. Bioinspir. Biomimet. 10:056011
    [Google Scholar]
  23. 23. 
    Gribovskiy A, Mondada F, Deneubourg JL, Cazenille L, Bredeche N, Halloy J 2015. Automated analysis of behavioural variability and filial imprinting of chicks (G. gallus), using autonomous robots. arXiv:1509.01957 [q-bio.QM]
  24. 24. 
    Gribovskiy A, Halloy J, Deneubourg JL, Mondada F 2018. Designing a socially integrated mobile robot for ethological research. Robot. Auton. Syst. 103:42–55
    [Google Scholar]
  25. 25. 
    Jolly L, Pittet F, Caudal JP, Mouret JB, Houdelier C et al. 2016. Animal-to-robot social attachment: initial requisites in a gallinaceous bird. Bioinspir. Biomimet. 11:016007
    [Google Scholar]
  26. 26. 
    Mondada F, Martinoli A, Correll N, Gribovskiy A, Halloy JI et al. 2013. A general methodology for the control of mixed natural-artificial societies. Handbook of Collective Robotics S Kernbach 547–86 Singapore: Pan Stanford
    [Google Scholar]
  27. 27. 
    Gribovskiy A, Halloy J, Deneubourg JL, Bleuler H, Mondada F 2010. Towards mixed societies of chickens and robots. 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems4722–28 Piscataway, NJ: IEEE
    [Google Scholar]
  28. 28. 
    Kim C, Ruberto T, Phamduy P, Porfiri M 2018. Closed-loop control of zebrafish behaviour in three dimensions using a robotic stimulus. Sci. Rep. 8:657
    [Google Scholar]
  29. 29. 
    Bonnet F, Binder S, de Oliveria ME, Halloy J, Mondada F 2014. A miniature mobile robot developed to be socially integrated with species of small fish. 2014 IEEE International Conference on Robotics and Biomimetics747–52 Piscataway, NJ: IEEE
    [Google Scholar]
  30. 30. 
    Couzin ID, Krause J, James R, Ruxton GD, Franks NR 2002. Collective memory and spatial sorting in animal groups. J. Theor. Biol. 218:1–11
    [Google Scholar]
  31. 31. 
    Papaspyros V, Bonnet F, Collignon B, Mondada F 2019. Bidirectional interactions facilitate the integration of a robot into a shoal of zebrafish Danio rerio. . PLOS ONE 14:e0220559
    [Google Scholar]
  32. 32. 
    Bonnet F, Mills R, Szopek M, Schönwetter-Fuchs S, Halloy J et al. 2019. Robots mediating interactions between animals for interspecies collective behaviors. Sci. Robot. 4:eaau7897
    [Google Scholar]
  33. 33. 
    Bonnet F, Gribovskiy A, Halloy J, Mondada F 2018. Closed-loop interactions between a shoal of zebrafish and a group of robotic fish in a circular corridor. Swarm Intell 12:227–44
    [Google Scholar]
  34. 34. 
    Mery F, Burns JG. 2010. Behavioural plasticity: an interaction between evolution and experience. Evol. Ecol. 24:571–83
    [Google Scholar]
  35. 35. 
    Snell-Rood EC. 2013. An overview of the evolutionary causes and consequences of behavioural plasticity. Anim. Behav. 85:1004–11
    [Google Scholar]
  36. 36. 
    Rankin CH, Abrams T, Barry RJ, Bhatnagar S, Clayton DF et al. 2009. Habituation revisited: an updated and revised description of the behavioral characteristics of habituation. Neurobiol. Learn. Mem. 92:135–38
    [Google Scholar]
  37. 37. 
    Peeke HVS, Herz MJ 2012. Habituation, Vol. 1: Behavioral Studies New York: Academic
    [Google Scholar]
  38. 38. 
    Blumstein DT. 2016. Habituation and sensitization: new thoughts about old ideas. Anim. Behav. 120:255–62
    [Google Scholar]
  39. 39. 
    Mikheev VN, Andreev OA. 1993. Two-phase exploration of a novel environment in the guppy. Poecilia reticulata. J. Fish Biol. 42:375–83
    [Google Scholar]
  40. 40. 
    Lukas J, Kalinkat G, Miesen FW, Landgraf T, Krause J, Bierbach D 2020. Consistent behavioral syndromes across seasons in an invasive freshwater fish. bioRxiv 2020.03.03.974998. https://doi.org/10.1101/2020.03.03.974998
  41. 41. 
    Kelley JL, Graves JA, Magurran AE 1999. Familiarity breeds contempt in guppies. Nature 401:661–62
    [Google Scholar]
  42. 42. 
    Brown C. 2001. Familiarity with the test environment improves escape responses in the crimson spotted rainbowfish, Melanotaenia duboulayi. Anim. Cogn. 4:109–13
    [Google Scholar]
  43. 43. 
    Bierbach D, Girndt A, Hamfler S, Klein M, Mücksch F et al. 2011. Male fish use prior knowledge about rivals to adjust their mate choice. Biol. Lett. 7:349–51
    [Google Scholar]
  44. 44. 
    Doran C, Bierbach D, Laskowski KL 2019. Familiarity increases aggressiveness among clonal fish. Anim. Behav. 148:153–59
    [Google Scholar]
  45. 45. 
    Bierbach D, Krause S, Romanczuk P, Lukas J, Arias-Rodriguez L, Krause J 2020. An interaction mechanism for the maintenance of fission–fusion dynamics under different individual densities. PeerJ 8:e8974
    [Google Scholar]
  46. 46. 
    Landgraf T, Moenck HJ, Gebhardt GHW, Weimar N, Hocke M et al. 2020. Socially competent robots: adaptation improves leadership performance in groups of live fish. arXiv:2009.06633 [cs.RO]
  47. 47. 
    Harris LT, Fiske ST. 2008. The brooms in Fantasia: neural correlates of anthropomorphizing objects. Soc. Cogn. 26:210–23
    [Google Scholar]
  48. 48. 
    Krach S, Hegel F, Wrede B, Sagerer G, Binkofski F, Kircher T 2008. Can machines think? Interaction and perspective taking with robots investigated via fMRI. PLOS ONE 3:e2597
    [Google Scholar]
  49. 49. 
    Waytz A, Morewedge CK, Epley N, Monteleone G, Gao JH, Cacioppo JT 2010. Making sense by making sentient: effectance motivation increases anthropomorphism. J. Pers. Soc. Psychol. 99:410–35
    [Google Scholar]
  50. 50. 
    Schillaci G, Bodiroža S, Hafner VV 2013. Evaluating the effect of saliency detection and attention manipulation in human-robot interaction. Int. J. Soc. Robot. 5:139–52
    [Google Scholar]
  51. 51. 
    Scheunemann MM, Salge C, Dautenhahn K 2019. Intrinsically motivated autonomy in human-robot interaction: human perception of predictive information in robots. Towards Autonomous Robotic Systems K Althoefer, J Konstantinova, K Zhang 325–37 Cham, Switz: Springer
    [Google Scholar]
  52. 52. 
    Schneider S, Kummert F. 2020. Comparing robot and human guided personalization: Adaptive exercise robots are perceived as more competent and trustworthy. Int. J. Soc. Robot. In press. https://doi.org/10.1007/s12369-020-00629-w
    [Crossref] [Google Scholar]
  53. 53. 
    Honig SS, Oron-Gilad T, Zaichyk H, Sarne-Fleischmann V, Olatunji S, Edan Y 2018. Toward socially aware person-following robots. IEEE Trans. Cogn. Dev. Syst. 10:936–54
    [Google Scholar]
  54. 54. 
    Sutton RS, Barto AG. 2018. Reinforcement Learning: An Introduction Cambridge, MA: MIT Press
  55. 55. 
    Rechenberg I. 1973. Evolutionsstrategie, Optimierung technischer Systeme nach Prinzipien der biologischen Evolution Stuttgart, Ger: Frommann-Holzboog
  56. 56. 
    Hansen N, Ostermeier A. 2001. Completely derandomized self-adaptation in evolution strategies. Evol. Comput 9:159–95
    [Google Scholar]
  57. 57. 
    Lizotte D, Wang T, Bowling M, Schuurmans D 2007. Automatic gait optimization with Gaussian process regression. Proceedings of the 20th International Joint Conference on Artificial Intelligence944–49 San Francisco: Morgan Kaufmann
    [Google Scholar]
  58. 58. 
    Wilson A, Fern A, Tadepalli P 2010. Incorporating domain models into Bayesian optimization for RL. Machine Learning and Knowledge Discovery in Databases JL Balcázar, F Bonchi, A Gionis, M Sebag 467–82 Berlin: Springer
    [Google Scholar]
  59. 59. 
    Stolle M, Precup D. 2002. Learning options in reinforcement learning. International Symposium on Abstraction, Reformulation, and Approximation S Koenig, RC Holte 212–23 Berlin: Springer
    [Google Scholar]
  60. 60. 
    Sutton RS, Precup D, Singh S 1999. Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning. Artif. Intell. 112:181–211
    [Google Scholar]
  61. 61. 
    Schaal S, Peters J, Nakanishi J, Ijspeert A 2005. Learning movement primitives. Robotics Research: The Eleventh International Symposium B Siciliano, O Khatib, P Dario, R Chatila 561–72 Berlin: Springer
    [Google Scholar]
  62. 62. 
    Ude A, Gams A, Asfour T, Morimoto J 2010. Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Trans. Robot. 26:800–15
    [Google Scholar]
  63. 63. 
    Dell AI, Bender JA, Branson K, Couzin ID, de Polavieja GG et al. 2014. Automated image-based tracking and its application in ecology. Trends Ecol. Evol. 29:417–28
    [Google Scholar]
  64. 64. 
    Tinbergen N. 1951. The Study of Instinct New York: Clarendon
  65. 65. 
    Romero-Ferrero F, Bergomi MG, Hinz RC, Heras FJH, de Polavieja GG 2019. idtracker.ai: tracking all individuals in small or large collectives of unmarked animals. Nat. Methods 16:179–82
    [Google Scholar]
  66. 66. 
    Pereira TD, Aldarondo DE, Willmore L, Kislin M, Wang SSH et al. 2019. Fast animal pose estimation using deep neural networks. Nat. Methods 16:117–25
    [Google Scholar]
  67. 67. 
    Mathis A, Mamidanna P, Cury KM, Abe T, Murthy VN et al. 2018. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat. Neurosci. 21:1281–89
    [Google Scholar]
  68. 68. 
    Graving JM, Chae D, Naik H, Li L, Koger B et al. 2019. DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning. eLife 8:e47994
    [Google Scholar]
  69. 69. 
    Dankert H, Wang L, Hoopfer ED, Anderson DJ, Perona P 2009. Automated monitoring and analysis of social behavior in Drosophila. Nat. Methods 6:297–303
    [Google Scholar]
  70. 70. 
    Branson K, Robie A, Bender J, Perona P, Dickinson M 2009. High-throughput ethomics in large groups of Drosophila. Nat. Methods 6:451–57
    [Google Scholar]
  71. 71. 
    Kain J, Stokes C, Gaudry Q, Song X, Foley J et al. 2013. Leg-tracking and automated behavioural classification in Drosophila. Nat. Commun 4:1910
    [Google Scholar]
  72. 72. 
    Berman GJ, Choi DM, Bialek W, Shaevitz JW 2014. Mapping the stereotyped behaviour of freely moving fruit flies. J. R. Soc. Interface 11:20140672
    [Google Scholar]
  73. 73. 
    Calhoun AJ, Pillow JW, Murthy M 2019. Unsupervised identification of the internal states that shape natural behavior. Nat. Neurosci. 22:2040–49
    [Google Scholar]
  74. 74. 
    Croft DP, James R, Krause J 2008. Exploring Animal Social Networks Princeton, NJ: Princeton Univ. Press
  75. 75. 
    Krause J, James R, Franks DW, Croft DP 2015. Animal Social Networks Oxford, UK: Oxford Univ. Press
  76. 76. 
    Lukeman R, Li YX, Edelstein-Keshet L 2010. Inferring individual rules from collective behavior. PNAS 107:12576–80
    [Google Scholar]
  77. 77. 
    Katz Y, Tunstrm K, Ioannou CC, Huepe C, Couzin ID 2011. Inferring the structure and dynamics of interactions in schooling fish. PNAS 108:18720–25
    [Google Scholar]
  78. 78. 
    Herbert-Read JE, Perna A, Mann RP, Schaerf TM, Sumpter DJT, Ward AJW 2011. Inferring the rules of interaction of shoaling fish. PNAS 108:18726–31
    [Google Scholar]
  79. 79. 
    Herbert-Read JE, Krause S, Morrell LJ, Schaerf TM, Krause J, Ward AJW 2013. The role of individuality in collective group movement. Proc. R. Soc. B 280:20122564
    [Google Scholar]
  80. 80. 
    Jolles JW, Boogert NJ, Sridhar VH, Couzin ID, Manica A 2017. Consistent individual differences drive collective behavior and group functioning of schooling fish. Curr. Biol. 27:2862–68.e7
    [Google Scholar]
  81. 81. 
    Jolles JW, King AJ, Killen SS 2020. The role of individual heterogeneity in collective animal behaviour. Trends Ecol. Evol. 35:278–91
    [Google Scholar]
  82. 82. 
    Nagy M, Ákos Z, Biro D, Vicsek T 2010. Hierarchical group dynamics in pigeon flocks. Nature 464:890–93
    [Google Scholar]
  83. 83. 
    Shannon C, Weaver W. 1998. The Mathematical Theory of Information Urbana: Univ. Ill. Press
  84. 84. 
    Schreiber T. 2000. Measuring information transfer. Phys. Rev. Lett. 85:461–64
    [Google Scholar]
  85. 85. 
    Lord WM, Sun J, Ouellette NT, Bollt EM 2016. Inference of causal information flow in collective animal behavior. IEEE Trans. Mol. Biol. Multi-Scale Commun. 2:107–16
    [Google Scholar]
  86. 86. 
    Sun J, Bollt EM. 2014. Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings. Phys. D 267:49–57
    [Google Scholar]
  87. 87. 
    Pilkiewicz KR, Lemasson BH, Rowland MA, Hein A, Sun J et al. 2020. Decoding collective communications using information theory tools. J. R. Soc. Interface 17:20190563
    [Google Scholar]
  88. 88. 
    Leonard NE. 2014. Multi-agent system dynamics: bifurcation and behavior of animal groups. Annu. Rev. Control 38:171–83
    [Google Scholar]
  89. 89. 
    Eyjolfsdottir E, Branson K, Yue Y, Perona P 2016. Learning recurrent representations for hierarchical behavior modeling. arXiv:1611.00094 [cs.AI]
  90. 90. 
    Shahrokhi S, Becker AT. 2016. Object manipulation and position control using a swarm with global inputs. 2016 IEEE International Conference on Automation Science and Engineering561–66 Piscataway, NJ: IEEE
    [Google Scholar]
  91. 91. 
    Mguni D, Jennings J, de Cote EM 2018. Decentralised learning in systems with many, many strategic agents. arXiv:1803.05028 [cs.MA]
  92. 92. 
    Gebhardt GHW, Daun K, Schnaubelt M, Neumann G 2018. Learning robust policies for object manipulation with robot swarms. 2018 IEEE International Conference on Robotics and Automation7688–95 Piscataway, NJ: IEEE
    [Google Scholar]
  93. 93. 
    Zaheer M, Kottur S, Ravanbakhsh S, Poczos B, Salakhutdinov RR, Smola AJ 2017. Deep sets. Advances in Neural Information Processing Systems 30 I Guyon, UV Luxburg, S Bengio, H Wallach, R Fergus et al.3391–401 Red Hook, NY: Curran
    [Google Scholar]
  94. 94. 
    Hüttenrauch M, Šožić A, Neumann G 2019. Deep reinforcement learning for swarm systems. arXiv:1807.06613 [cs.MA]
  95. 95. 
    Gebhardt GHW. 2019. Using mean embeddings for state estimation and reinforcement learning PhD Thesis, Tech. Univ. Darmstadt Darmstadt, Ger:.
  96. 96. 
    Aberdeen D. 2003. A (revised) survey of approximate methods for solving partially observable Markov decision processes Tech. Rep., Natl. Inf. Commun. Technol. Aust Canberra:
  97. 97. 
    Wierstra D, Foerster A, Peters J, Schmidhuber J 2007. Solving deep memory POMDPs with recurrent policy gradients. Artificial Neural Networks: ICANN 2007 JM de Sá, LA Alexandre, W Duch, D Mandic 697–706 Berlin: Springer
    [Google Scholar]
  98. 98. 
    Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw 61:85–117
    [Google Scholar]
  99. 99. 
    Tobin J, Fong R, Ray A, Schneider J, Zaremba W, Abbeel P 2017. Domain randomization for transferring deep neural networks from simulation to the real world. arXiv:1703.06907 [cs.RO]
  100. 100. 
    Fu J, Levine S, Abbeel P 2016. One-shot learning of manipulation skills with online dynamics adaptation and neural network priors. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems4019–26 Piscataway, NJ: IEEE
    [Google Scholar]
  101. 101. 
    Isele D, Rostami M, Eaton E 2016. Using task features for zero-shot knowledge transfer in lifelong learning. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence1620–26 New York: AAAI Press
    [Google Scholar]
  102. 102. 
    Finn C, Abbeel P, Levine S 2017. Model-agnostic meta-learning for fast adaptation of deep networks. Proceedings of the 34th International Conference on Machine Learning1126–35 Proc. Mach. Learn. Res. Vol. 70. N.p.: PMLR
    [Google Scholar]
  103. 103. 
    Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L 2018. Explaining explanations: an overview of interpretability of machine learning. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics80–89 Piscataway, NJ: IEEE
    [Google Scholar]
  104. 104. 
    Réale D, Reader SM, Sol D, McDougall PT, Dingemanse NJ 2007. Integrating animal temperament within ecology and evolution. Biol. Rev. 82:291–318
    [Google Scholar]
  105. 105. 
    Freund J, Brandmaier AM, Lewejohann L, Kirste I, Kritzler M et al. 2013. Emergence of individuality in genetically identical mice. Science 340:756–59
    [Google Scholar]
  106. 106. 
    Bierbach D, Laskowski KL, Wolf M 2017. Behavioural individuality in clonal fish arises despite near-identical rearing conditions. Nat. Commun. 8:15361
    [Google Scholar]
  107. 107. 
    Deisenroth M, Rasmussen CE. 2011. PILCO: a model-based and data-efficient approach to policy search. Proceedings of the 28th International Conference on Machine Learning465–72 Madison, WI: Omnipress
    [Google Scholar]
  108. 108. 
    Gal Y, McAllister R, Rasmussen CE 2016. Improving PILCO with Bayesian neural network dynamics models Paper presented at the Data-Efficient Machine Learning Workshop, 33rd International Conference on Machine Learning New York: June 19–24
  109. 109. 
    Ha D, Schmidhuber J. 2018. World models. arXiv:1803.10122 [cs.LG]
  110. 110. 
    Oudeyer PY, Kaplan F, Hafner VV 2007. Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11:265–86
    [Google Scholar]
  111. 111. 
    Pathak D, Agrawal P, Efros AA, Darrell T 2017. Curiosity-driven exploration by self-supervised prediction. 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops488–89 Piscataway, NJ: IEEE
    [Google Scholar]
  112. 112. 
    Schmidhuber J. 2006. Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connect. Sci. 18:173–87
    [Google Scholar]
  113. 113. 
    Schmidhuber J. 2007. Simple algorithmic principles of discovery, subjective beauty, selective attention, curiosity & creativity. Discovery Science: 10th International Conference, DS 2007 V Corruble, M Takeda, E Suzuki 26–38 Berlin: Springer
    [Google Scholar]
  114. 114. 
    Schmidhuber J. 2010. Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Trans. Auton. Ment. Dev. 2:230–47
    [Google Scholar]
  115. 115. 
    Couzin ID, Krause J, Franks NR, Levin SA 2005. Effective leadership and decision-making in animal groups on the move. Nature 433:513–16
    [Google Scholar]
  116. 116. 
    Ioannou C, Singh M, Couzin I 2015. Potential leaders trade off goal-oriented and socially oriented behavior in mobile animal groups. Am. Nat. 186:284–93
    [Google Scholar]
  117. 117. 
    Frohnwieser A, Murray JC, Pike TW, Wilkinson A 2016. Using robots to understand animal cognition: robots in animal cognition. J. Exp. Anal. Behav. 105:14–22
    [Google Scholar]
  118. 118. 
    Goodale E, Beauchamp G, Magrath RD, Nieh JC, Ruxton GD 2010. Interspecific information transfer influences animal community structure. Trends Ecol. Evol. 25:354–361
    [Google Scholar]
  119. 119. 
    Bieker K, Peitz S, Brunton SL, Kutz JN, Dellnitz M 2019. Deep model predictive control with online learning for complex physical systems. arXiv:1905.10094 [cs.LG]
  120. 120. 
    Wülfing JM, Kumar SS, Boedecker J, Riedmiller M, Egert U 2019. Adaptive long-term control of biological neural networks with deep reinforcement learning. Neurocomputing 342:66–74
    [Google Scholar]
  121. 121. 
    Tessadori J, Chiappalone M. 2015. Closed-loop neuro-robotic experiments to test computational properties of neuronal networks. J. Vis. Exp. 97:42341
    [Google Scholar]

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