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Annual Review of Control, Robotics, and Autonomous Systems

Volume 6, 2023

On the Timescales of Embodied Intelligence for Autonomous Adaptive Systems

Abstract

Embodiment is a crucial concept for the autonomy and adaptivity of systems working in the physical world with high degrees of uncertainty and complexity. The physical bodies of autonomous adaptive systems heavily influence the information flow from the environment to the central processing (and vice versa), requiring us to consider the full triad of brain, body, and environment to investigate intelligent behavior. This article provides a structured review of embodied intelligence with a special emphasis on the concept of timescales and their role in self-organization and the emergence of complex behavior. We classify embodied interactions into three types—cross-timescale matching, separation, and nontemporal sequences—and discuss how these interactions were studied in the past as well as how they can contribute to the systematic investigation of complex autonomous and adaptive systems in both biological and artificial entities.

Keyword(s): autonomous adaptive systems, bioinspired robotics, cross-timescale interactions, embodiment
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2023-05-03
2024-04-16
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Literature Cited

  1. 1.
    Pfeifer R, Bongard J. 2007. How the Body Shapes the Way We Think: A New View of Intelligence Cambridge, MA: MIT Press
  2. 2.
    Descartes R. 1999. Discourse on Method and Meditations on First Philosophy Transl. DA Cress Indianapolis, IN: Hackett. , 4th ed..
  3. 3.
    Varela FJ, Thompson E, Rosch E. 2017. The Embodied Mind: Cognitive Science and Human Experience Cambridge, MA: MIT Press. , Rev. Ed..
  4. 4.
    Laschi C, Cianchetti M, Mazzolai B, Margheri L, Follador M, Dario P 2012. Soft robot arm inspired by the octopus. Adv. Robot. 26:709–27
     [Google Scholar]
  5. 5.
    Hara F, Pfeifer R, eds. 2003. Morpho-Functional Machines: The New Species; Designing Embodied Intelligence Tokyo: Springer
  6. 6.
    Cianchetti M, Follador M, Mazzolai B, Dario P, Laschi C 2012. Design and development of a soft robotic octopus arm exploiting embodied intelligence. 2012 IEEE International Conference on Robotics and Automation5271–76. Piscataway, NJ: IEEE
     [Google Scholar]
  7. 7.
    Howard D, Eiben AE, Kennedy DF, Mouret JB, Valencia P, Winkler D. 2019. Evolving embodied intelligence from materials to machines. Nat. Mach. Intell. 1:12–19
     [Google Scholar]
  8. 8.
    Weng YH, Ho CH. 2020. Embodiment and algorithms for human–robot interaction. The Cambridge Handbook of the Law of Algorithms W Barfield 736–56. Cambridge, UK: Cambridge Univ. Press
     [Google Scholar]
  9. 9.
    Moulin-Frier C, Puigbo JY, Arsiwalla XD, Sanchez-Fibla M, Verschure PF. 2017. Embodied artificial intelligence through distributed adaptive control: an integrated framework. 2017 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics324–30. Piscataway, NJ: IEEE
     [Google Scholar]
  10. 10.
    Glenberg AM. 2010. Embodiment as a unifying perspective for psychology. WIREs Cogn. Sci. 1:586–96
     [Google Scholar]
  11. 11.
    De Vega M, Glenberg A, Graesser A. 2012. Symbols and Embodiment: Debates on Meaning and Cognition Oxford, UK: Oxford Univ. Press
  12. 12.
    MacWhinney B 2013. The emergence of language from embodiment. The Emergence of Language B MacWhinney 213–56. Hove, UK: Psychol. Press
     [Google Scholar]
  13. 13.
    Maturana HR, Varela FJ. 2012. Autopoiesis and Cognition: The Realization of the Living Dordrecht, Neth: Springer
  14. 14.
    Bresee CS. 2018. Embodiment in the mammalian whisker system: how anatomy and biomechanics facilitate sensation and movement PhD Thesis, Northwest. Univ. Evanston, IL:
  15. 15.
    Keijzer FA. 2017. Evolutionary convergence and biologically embodied cognition. Interface Focus 7:20160123
     [Google Scholar]
  16. 16.
    Pfeifer R, Lungarella M, Iida F. 2007. Self-organization, embodiment, and biologically inspired robotics. Science 318:1088–93
     [Google Scholar]
  17. 17.
    Núñez RE 2008. Mathematics, the ultimate challenge to embodiment: truth and the grounding of axiomatic systems. Handbook of Cognitive Science: An Embodied Approach P Calvo, A Gomila 333–53. San Diego, CA: Elsevier
     [Google Scholar]
  18. 18.
    Miriyev A, Stack K, Lipson H. 2017. Soft material for soft actuators. Nat. Commun. 8:596
     [Google Scholar]
  19. 19.
    Iida F, Ijspeert A 2016. Biologically inspired robotics. Springer Handbook of Robotics B Siciliano, O Khatib 2015–34. Berlin: Springer
     [Google Scholar]
  20. 20.
    Pfeifer R, Scheier C. 2001. Understanding Intelligence Cambridge, MA: MIT Press
  21. 21.
    Ashby WR. 1960. Design for a Brain London: Chapman & Hall
  22. 22.
    Turing AM. 1952. The chemical basis of morphogenesis. Philos. Trans. R. Soc. 237:37–72
     [Google Scholar]
  23. 23.
    von Neumann J. 1966. The Theory of Self-Reproducing Automata Urbana: Univ. Ill. Press
  24. 24.
    Langton C. 1997. Artificial Life: An Overview Cambridge, MA: MIT Press
  25. 25.
    Nolfi S, Floreano D. 2000. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines Cambridge, MA: MIT Press
  26. 26.
    Lungarella M, Metta G, Pfeifer R, Sandini G. 2003. Developmental robotics: a survey. Connect. Sci. 15:151–90
     [Google Scholar]
  27. 27.
    Arkin R. 1998. Behavior-Based Robotics Cambridge, MA: MIT Press
  28. 28.
    Laschi C, Mazzolai B, Cianchetti M. 2016. Soft robotics: technologies and systems pushing the boundaries of robot abilities. Sci. Robot. 1:eaah3690
     [Google Scholar]
  29. 29.
    Vogel S. 2013. Comparative Biomechanics: Life's Physical World Princeton, NJ: Princeton Univ. Press
  30. 30.
    Bear MF, Connors BW, Paradiso MA. 2016. Neuroscience: Exploring the Brain Philadelphia: Wolters Kluwer. , 4th ed..
  31. 31.
    Thompson DW, Thompson DW. 1942. On Growth and Form, Vol. 2 Cambridge, UK: Cambridge Univ. Press
  32. 32.
    Ijspeert AJ. 2014. Biorobotics: using robots to emulate and investigate agile locomotion. Science 346:196–203
     [Google Scholar]
  33. 33.
    Hughes JAE, Maiolino P, Iida F. 2018. An anthropomorphic soft skeleton hand exploiting conditional models for piano playing. Sci. Robot. 3:eaau3098
     [Google Scholar]
  34. 34.
    Mazzolai B, Beccai L, Mattoli V. 2014. Plants as model in biomimetics and biorobotics: new perspectives. Front. Bioeng. Biotechnol. 2:2
     [Google Scholar]
  35. 35.
    Milo R, Phillips R. 2015. Cell Biology by the Numbers New York: Garland Sci.
  36. 36.
    Haldane JBS. 1949. Suggestions as to quantitative measurement of rates of evolution. Evolution 3:51–56
     [Google Scholar]
  37. 37.
    Jones AT. 1919.. “ Working up” in a swing. Science 50:20–21
     [Google Scholar]
  38. 38.
    Geyer H, Seyfarth A, Blickhan R. 2006. Compliant leg behaviour explains basic dynamics of walking and running. Proc. R. Soc. B 273:2861–67
     [Google Scholar]
  39. 39.
    Milton J, Cabrera JL, Ohira T, Tajima S, Tonosaki Y et al. 2009. The time-delayed inverted pendulum: implications for human balance control. Chaos 19:026110
     [Google Scholar]
  40. 40.
    Holmes PJ. 1982. The dynamics of repeated impacts with a sinusoidally vibrating table. J. Sound Vib. 84:173–89
     [Google Scholar]
  41. 41.
    Spong MW 1998. Underactuated mechanical systems. Control Problems in Robotics and Automation B Siciliano, KP Valavanis 135–50. Berlin: Springer
     [Google Scholar]
  42. 42.
    Kuo AD, Donelan JM, Ruina A. 2005. Energetic consequences of walking like an inverted pendulum: step-to-step transitions. Exerc. Sport Sci. Rev. 33:88–97
     [Google Scholar]
  43. 43.
    Goswami A, Espiau B, Keramane A. 1997. Limit cycles in a passive compass gait biped and passivity-mimicking control laws. Auton. Robots 4:273–86
     [Google Scholar]
  44. 44.
    Garcia M, Chatterjee A, Ruina A, Coleman M. 1998. The simplest walking model: stability, complexity, and scaling. J. Biomech. Eng. 120:281–88
     [Google Scholar]
  45. 45.
    McGeer T. 1990. Passive dynamic walking. Int. J. Robot. Res. 9:62–82
     [Google Scholar]
  46. 46.
    Collins SH, Wisse M, Ruina A. 2001. A three-dimensional passive-dynamic walking robot with two legs and knees. Int. J. Robot. Res. 20:607–15
     [Google Scholar]
  47. 47.
    Collins S, Ruina A, Tedrake R, Wisse M. 2005. Efficient bipedal robots based on passive-dynamic walkers. Science 307:1082–85
     [Google Scholar]
  48. 48.
    Reist P, D'Andrea R. 2012. Design and analysis of a blind juggling robot. IEEE Trans. Robot. 28:1228–43
     [Google Scholar]
  49. 49.
    Ruina A. 2009. A three-dimensional passive-dynamic walking robot with two legs and knees. Cornell University Biorobotics and Locomotion Lab http://ruina.tam.cornell.edu/research/topics/locomotion_and_robotics/3d_passive_dynamic
    [Google Scholar]
  50. 50.
    Inst. Dyn. Syst. Control. 2022. The Blind Juggler. ETH Zurich Institute for Dynamic Systems and Control https://www.blindjuggler.org/the-blind-juggler
    [Google Scholar]
  51. 51.
    Bhounsule PA, Cortell J, Grewal A, Hendriksen B, Karssen JD et al. 2014. Low-bandwidth reflex-based control for lower power walking: 65 km on a single battery charge. Int. J. Robot. Res. 33:1305–21
     [Google Scholar]
  52. 52.
    Usherwood JR, Bertram JE. 2003. Understanding brachiation: insight from a collisional perspective. J. Exp. Biol. 206:1631–42
     [Google Scholar]
  53. 53.
    Giardina F, Mahadevan L. 2021. Models of benthic bipedalism. J. R. Soc. Interface 18:20200701
     [Google Scholar]
  54. 54.
    Gazzola M, Argentina M, Mahadevan L 2014. Scaling macroscopic aquatic locomotion. Nat. Phys. 10:758–61
     [Google Scholar]
  55. 55.
    Ronsse R, Lefevre P, Sepulchre R. 2007. Rhythmic feedback control of a blind planar juggler. IEEE Trans. Robot. 23:790–802
     [Google Scholar]
  56. 56.
    Karlson P, Butenandt A. 1959. Pheromones (ectohormones) in insects. Annu. Rev. Entomol. 4:39–58
     [Google Scholar]
  57. 57.
    Theraulaz G, Bonabeau E. 1999. A brief history of stigmergy. Artif. Life 5:97–116
     [Google Scholar]
  58. 58.
    Delsuc F. 2003. Army ants trapped by their evolutionary history. PLOS Biol. 1:e37
     [Google Scholar]
  59. 59.
    Fujisawa R, Dobata S, Kubota D, Imamura H, Matsuno F 2008. Dependency by concentration of pheromone trail for multiple robots. International Conference on Ant Colony Optimization and Swarm Intelligence M Dorigo, M Birattari, C Blum, M Clerc, T Stützle 283–90. Berlin: Springer
     [Google Scholar]
  60. 60.
    Prasath SG, Mandal S, Giardina F, Kennedy J, Murthy VN, Mahadevan L. 2021. Cooperative escape in ants and robots. bioRxiv 2021.07.12.451633. https://doi.org/10.1101/2021.07.12.451633
  61. 61.
    Sugawara K, Kazama T, Watanabe T. 2004. Foraging behavior of interacting robots with virtual pheromone. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 33074–79. Piscataway, NJ: IEEE
     [Google Scholar]
  62. 62.
    Strogatz SH. 2000. From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators. Phys. D 143:1–20
     [Google Scholar]
  63. 63.
    Ijspeert AJ. 2008. Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21:642–53
     [Google Scholar]
  64. 64.
    Ijspeert AJ, Crespi A, Ryczko D, Cabelguen JM. 2007. From swimming to walking with a salamander robot driven by a spinal cord model. Science 315:1416–20
     [Google Scholar]
  65. 65.
    Shepherd RF, Ilievski F, Choi W, Morin SA, Stokes AA et al. 2011. Multigait soft robot. PNAS 108:20400–3
     [Google Scholar]
  66. 66.
    Evans M, Fox CW, Pearson MJ, Lepora NF, Prescott TJ. 2010. Whisker-object contact speed affects radial distance estimation. 2010 IEEE International Conference on Robotics and Biomimetics720–25. Piscataway, NJ: IEEE
     [Google Scholar]
  67. 67.
    Xu D, Loeb GE, Fishel JA. 2013. Tactile identification of objects using Bayesian exploration. 2013 IEEE International Conference on Robotics and Automation3056–61. Piscataway, NJ: IEEE
     [Google Scholar]
  68. 68.
    Thandiackal R, Melo K, Paez L, Herault J, Kano T et al. 2021. Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing. Sci. Robot. 6:eabf6354
     [Google Scholar]
  69. 69.
    Owaki D, Goda M, Miyazawa S, Ishiguro A. 2017. A minimal model describing hexapedal interlimb coordination: the Tegotae-based approach. Front. Neurorobot. 11:29
     [Google Scholar]
  70. 70.
    Bonabeau E, Theraulaz G, Deneubourg JL, Franks NR, Rafelsberger O et al. 1998. A model for the emergence of pillars, walls and royal chambers in termite nests. Philos. Trans. R. Soc. B 353:1561–76
     [Google Scholar]
  71. 71.
    Petersen KH, Napp N, Stuart-Smith R, Rus D, Kovac M. 2019. A review of collective robotic construction. Sci. Robot. 4:eaau8479
     [Google Scholar]
  72. 72.
    Farkas I, Helbing D, Vicsek T. 2002. Mexican waves in an excitable medium. Nature 419:131–32
     [Google Scholar]
  73. 73.
    Pasquale V, Massobrio P, Bologna L, Chiappalone M, Martinoia S. 2008. Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience 153:1354–69
     [Google Scholar]
  74. 74.
    Kastberger G, Weihmann F, Hoetzl T. 2010. Complex social waves of giant honeybees provoked by a dummy wasp support the special-agent hypothesis. Commun. Integr. Biol. 3:179–80
     [Google Scholar]
  75. 75.
    Drossel B, Schwabl F. 1992. Self-organized critical forest-fire model. Phys. Rev. Lett. 69:1629–32
     [Google Scholar]
  76. 76.
    Kapitza PL. 1965. Dynamical stability of a pendulum when its point of suspension vibrates, and pendulum with a vibrating suspension. The Collected Papers of P.L. Kapitza, Vol. 2: 1938–1964 D ter Haar pp.714–37. Oxford, UK: Pergamon
     [Google Scholar]
  77. 77.
    Landau LD, Lifshitz EM. 1960. Mechanics Oxford, UK: Pergamon
  78. 78.
    Strogatz SH, Abrams DM, McRobie A, Eckhardt B, Ott E. 2005. Crowd synchrony on the Millennium Bridge. Nature 438:43–44
     [Google Scholar]
  79. 79.
    Chen Y, Zhao H, Mao J, Chirarattananon P, Helbling EF et al. 2019. Controlled flight of a microrobot powered by soft artificial muscles. Nature 575:324–29
     [Google Scholar]
  80. 80.
    Brooks R. 1986. A robust layered control system for a mobile robot. IEEE J. Robot. Autom. 2:14–23
     [Google Scholar]
  81. 81.
    Walter WG. 1950. An imitation of life. Sci. Am. 182:42–45
     [Google Scholar]
  82. 82.
    Braitenberg V. 1986. Vehicles: Experiments in Synthetic Psychology Cambridge, MA: MIT Press
  83. 83.
    Merel J, Botvinick M, Wayne G. 2019. Hierarchical motor control in mammals and machines. Nat. Commun. 10:5489
     [Google Scholar]
  84. 84.
    Salomaa A. 2014. Theory of Automata Oxford, UK: Pergamon
  85. 85.
    Turing AM. 1936. On computable numbers, with an application to the Entscheidungsproblem. J. Math. 58:5
     [Google Scholar]
  86. 86.
    Ashar P, Devadas S, Djaloeis A, Newton AR. 1992. Sequential Logic Synthesis New York: Springer
  87. 87.
    Purcell EM. 1977. Life at low Reynolds number. Am. J. Phys. 45:3–11
     [Google Scholar]
  88. 88.
    Lauga E. 2011. Life around the scallop theorem. Soft Matter 7:3060–65
     [Google Scholar]
  89. 89.
    Ostrowski J, Burdick J. 1998. The geometric mechanics of undulatory robotic locomotion. Int. J. Robot. Res. 17:683–701
     [Google Scholar]
  90. 90.
    Chopard B, Droz M. 1998. Cellular Automata Modeling of Physical Systems Cambridge, UK: Cambridge Univ. Press
  91. 91.
    Wolfram S 2018. Cellular Automata and Complexity: Collected Papers Boca Raton, FL: CRC
  92. 92.
    Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O. 1995. Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett. 75:1226
     [Google Scholar]
  93. 93.
    LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436–44
     [Google Scholar]
  94. 94.
    Rumelhart DE, Hinton GE, Williams RJ. 1986. Learning representations by back-propagating errors. Nature 323:533–36
     [Google Scholar]
  95. 95.
    Aubin CA, Gorissen B, Milana E, Buskohl PR, Lazarus N et al. 2022. Towards enduring autonomous robots via embodied energy. Nature 602:393–402
     [Google Scholar]
  96. 96.
    Kuo AD. 2007. Choosing your steps carefully. IEEE Robot. Autom. Mag. 14:218–29
     [Google Scholar]
  97. 97.
    Gabrielli G. 1950. What price speed?. Mech. Eng. 72:775–81
     [Google Scholar]
  98. 98.
    Radhakrishnan V. 1998. Locomotion: dealing with friction. PNAS 95:5448–55
     [Google Scholar]
  99. 99.
    Kuo AD. 2007. The six determinants of gait and the inverted pendulum analogy: a dynamic walking perspective. Hum. Mov. Sci. 26:617–56
     [Google Scholar]
  100. 100.
    Mettin U, La Hera PX, Freidovich LB, Shiriaev AS 2010. Parallel elastic actuators as a control tool for preplanned trajectories of underactuated mechanical systems. Int. J. Robot. Res. 29:1186–98
     [Google Scholar]
  101. 101.
    Häufle DF, Taylor M, Schmitt S, Geyer H. 2012. A clutched parallel elastic actuator concept: towards energy efficient powered legs in prosthetics and robotics. 2012 4th IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics1614–19. Piscataway, NJ: IEEE
     [Google Scholar]
  102. 102.
    Guenther F, Vu HQ, Iida F. 2019. Improving legged robot hopping by using coupling-based series elastic actuation. IEEE/ASME Trans. Mechatron. 24:413–23
     [Google Scholar]
  103. 103.
    Guenther F, Iida F. 2016. Energy-efficient monopod running with a large payload based on open-loop parallel elastic actuation. IEEE Trans. Robot. 33:102–13
     [Google Scholar]
  104. 104.
    Vu HQ, Yu X, Iida F, Pfeifer R. 2015. Improving energy efficiency of hopping locomotion by using a variable stiffness actuator. IEEE/ASME Trans. Mechatron. 21:472–86
     [Google Scholar]
  105. 105.
    Wolf S, Grioli G, Eiberger O, Friedl W, Grebenstein M et al. 2015. Variable stiffness actuators: review on design and components. IEEE/ASME Trans. Mechatron. 21:2418–30
     [Google Scholar]
  106. 106.
    Collins SH, Wiggin MB, Sawicki GS. 2015. Reducing the energy cost of human walking using an unpowered exoskeleton. Nature 522:212–15
     [Google Scholar]
  107. 107.
    Heglund NC, Taylor CR. 1988. Speed, stride frequency and energy cost per stride: How do they change with body size and gait?. J. Exp. Biol. 138:301–18
     [Google Scholar]
  108. 108.
    Alexander RM. 1984. The gaits of bipedal and quadrupedal animals. Int. J. Robot. Res. 3:49–59
     [Google Scholar]
  109. 109.
    Hreljac A. 1993. Preferred and energetically optimal gait transition speeds in human locomotion. Med. Sci. Sports Exerc. 25:1158–62
     [Google Scholar]
  110. 110.
    Iida F, Rummel J, Seyfarth A. 2008. Bipedal walking and running with spring-like biarticular muscles. J. Biomech. 41:656–67
     [Google Scholar]
  111. 111.
    Pfeifer R, Scheier C. 1997. Sensory-motor coordination: the metaphor and beyond. Robot. Auton. Syst. 20:157–78
     [Google Scholar]
  112. 112.
    Diamond ME, Von Heimendahl M, Knutsen PM, Kleinfeld D, Ahissar E 2008.. ‘ Where’ and ‘what’ in the whisker sensorimotor system. Nat. Rev. Neurosci. 9:601–12
     [Google Scholar]
  113. 113.
    Mitchinson B, Martin CJ, Grant RA, Prescott TJ. 2007. Feedback control in active sensing: rat exploratory whisking is modulated by environmental contact. Proc. R. Soc. B 274:1035–41
     [Google Scholar]
  114. 114.
    Jung D, Zelinsky A. 1996. Whisker based mobile robot navigation. Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems: IROS'96, Vol. 2497–504. Piscataway, NJ: IEEE
     [Google Scholar]
  115. 115.
    Russell RA, Wijaya JA. 2003. Object location and recognition using whisker sensors. Australasian Conference on Robotics and Automation761–68. Sydney: Aust. Robot. Autom. Assoc.
     [Google Scholar]
  116. 116.
    Yu Z, Sadati SH, Hauser H, Childs PR, Nanayakkara T. 2022. A semi-supervised reservoir computing system based on tapered whisker for mobile robot terrain identification and roughness estimation. IEEE Robot. Autom. Lett. 7:5655–62
     [Google Scholar]
  117. 117.
    Schmitz A, Maiolino P, Maggiali M, Natale L, Cannata G, Metta G. 2011. Methods and technologies for the implementation of large-scale robot tactile sensors. IEEE Trans. Robot. 27:389–400
     [Google Scholar]
  118. 118.
    Dahiya RS, Metta G, Valle M, Sandini G. 2009. Tactile sensing-from humans to humanoids. IEEE Trans. Robot. 26:1–20
     [Google Scholar]
  119. 119.
    Park YL, Chen BR, Wood RJ. 2012. Design and fabrication of soft artificial skin using embedded microchannels and liquid conductors. IEEE Sens. J. 12:2711–18
     [Google Scholar]
  120. 120.
    Sornkarn N, Dasgupta P, Nanayakkara T. 2016. Morphological computation of haptic perception of a controllable stiffness probe. PLOS ONE 11:e0156982
     [Google Scholar]
  121. 121.
    Scimeca L, Hughes J, Maiolino P, He L, Nanayakkara T, Iida F. 2022. Action augmentation of tactile perception for soft-body palpation. Soft Robot. 9:280–92
     [Google Scholar]
  122. 122.
    Taga G, Yamaguchi Y, Shimizu H. 1991. Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment. Biol. Cybernet. 65:147–59
     [Google Scholar]
  123. 123.
    O'Keeffe KP, Hong H, Strogatz SH. 2017. Oscillators that sync and swarm. Nat. Commun. 8:1504
     [Google Scholar]
  124. 124.
    Endo G, Morimoto J, Matsubara T, Nakanishi J, Cheng G. 2008. Learning CPG-based biped locomotion with a policy gradient method: application to a humanoid robot. Int. J. Robot. Res. 27:213–28
     [Google Scholar]
  125. 125.
    Yasui K, Kano T, Standen EM, Aonuma H, Ijspeert AJ, Ishiguro A. 2019. Decoding the essential interplay between central and peripheral control in adaptive locomotion of amphibious centipedes. Sci. Rep. 9:18288
     [Google Scholar]
  126. 126.
    Pearson K, Gordon J 2000. Spinal reflexes. Principles of Neural Science ER Kandel, JH Schwartz, TM Jessell 713–36. New York: McGraw-Hill. , 4th ed..
     [Google Scholar]
  127. 127.
    Geyer H, Herr H. 2010. A muscle-reflex model that encodes principles of legged mechanics produces human walking dynamics and muscle activities. IEEE Trans. Neural Syst. Rehabil. Eng. 18:263–73
     [Google Scholar]
  128. 128.
    Marques HG, Bharadwaj A, Iida F. 2014. From spontaneous motor activity to coordinated behaviour: a developmental model. PLOS Comput. Biol. 10:e1003653
     [Google Scholar]
  129. 129.
    Espenschied KS, Quinn RD, Beer RD, Chiel HJ. 1996. Biologically based distributed control and local reflexes improve rough terrain locomotion in a hexapod robot. Robot. Auton. Syst. 18:59–64
     [Google Scholar]
  130. 130.
    Bekey G, Tomovic R. 1986. Robot control by reflex actions. Proceedings of the 1986 IEEE International Conference on Robotics and Automation, Vol. 3240–47. Piscataway, NJ: IEEE
     [Google Scholar]
  131. 131.
    Park JH, Kwon O. 2001. Reflex control of biped robot locomotion on a slippery surface. Proceedings of the 2001 ICRA: IEEE International Conference on Robotics and Automation, Vol. 44134–39. Piscataway, NJ: IEEE
     [Google Scholar]
  132. 132.
    Gerstner W, Kistler WM. 2002. Mathematical formulations of Hebbian learning. Biol. Cybernet. 87:404–15
     [Google Scholar]
  133. 133.
    Pfeifer R, Iida F, Lungarella M. 2014. Cognition from the bottom up: on biological inspiration, body morphology, and soft materials. Trends Cogn. Sci. 18:404–13
     [Google Scholar]
  134. 134.
    Hauser H, Ijspeert AJ, Füchslin RM, Pfeifer R, Maass W. 2011. Towards a theoretical foundation for morphological computation with compliant bodies. Biol. Cybernet. 105:355–70
     [Google Scholar]
  135. 135.
    Loeb GE, Brown IE, Cheng EJ. 1999. A hierarchical foundation for models of sensorimotor control. Exp. Brain Res. 126:1–18
     [Google Scholar]
  136. 136.
    Pratt GA, Williamson MM. 1995. Series elastic actuators. Proceedings of the 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems: Human Robot Interaction and Cooperative Robots, Vol. 1399–406. Piscataway, NJ: IEEE
     [Google Scholar]
  137. 137.
    Hutter M, Remy CD, Hoepflinger MA, Siegwart R 2012. High compliant series elastic actuation for the robotic leg ScarlETH. Field Robotics P Bidaud, MO Tokhi, C Grand, CS Virk 507–14. Singapore: World Sci.
     [Google Scholar]
  138. 138.
    Haddadin S, Croft E 2016. Physical human–robot interaction. Springer Handbook of Robotics B Siciliano, O Khatib 1835–74. Berlin: Springer
     [Google Scholar]
  139. 139.
    Nakajima K. 2020. Physical reservoir computing—an introductory perspective. Jpn. J. Appl. Phys. 59:060501
     [Google Scholar]
  140. 140.
    Jaeger H. 2002. Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach GMD Rep. 159 Ger. Natl. Res. Cent. Inf. Technol., Sankt Augustin Ger.:
  141. 141.
    Nakajima K, Hauser H, Li T, Pfeifer R. 2015. Information processing via physical soft body. Sci. Rep. 5:10487
     [Google Scholar]
  142. 142.
    Caluwaerts K, D'Haene M, Verstraeten D, Schrauwen B 2013. Locomotion without a brain: physical reservoir computing in tensegrity structures. Artif. Life 19:35–66
     [Google Scholar]
  143. 143.
    Zhao Q, Nakajima K, Sumioka H, Hauser H, Pfeifer R. 2013. Spine dynamics as a computational resource in spine-driven quadruped locomotion. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems1445–51. Piscataway, NJ: IEEE
     [Google Scholar]
  144. 144.
    Samuelsen GB, Larsen KB, Bogdanovic N, Laursen H, Græm N et al. 2003. The changing number of cells in the human fetal forebrain and its subdivisions: a stereological analysis. Cereb. Cortex 13:115–22
     [Google Scholar]
  145. 145.
    Murphy RR. 2015. Meta-analysis of autonomy at the DARPA Robotics Challenge trials. J. Field Robot. 32:189–91
     [Google Scholar]
  146. 146.
    Eppner C, Höfer S, Jonschkowski R, Martín-Martín R, Sieverling A et al. 2018. Four aspects of building robotic systems: lessons from the Amazon Picking Challenge; 2015. Auton. Robots 42:1459–75
     [Google Scholar]
  147. 147.
    Simon HA. 1980. Cognitive science: the newest science of the artificial. Cogn. Sci. 4:33–46
     [Google Scholar]
  148. 148.
    Dawkins R. 2016. The Extended Phenotype: The Long Reach of the Gene Oxford, UK: Oxford Univ. Press
  149. 149.
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput. 9:1735–80
     [Google Scholar]
  150. 150.
    Hughes J, Culha U, Giardina F, Guenther F, Rosendo A, Iida F 2016. Soft manipulators and grippers: a review. Front. Robot. AI 3:69
     [Google Scholar]
  151. 151.
    Del Dottore E, Sadeghi A, Mondini A, Mattoli V, Mazzolai B. 2018. Toward growing robots: a historical evolution from cellular to plant-inspired robotics. Front. Robot. AI 5:16
     [Google Scholar]
  152. 152.
    Terryn S, Langenbach J, Roels E, Brancart J, Bakkali-Hassani C et al. 2021. A review on self-healing polymers for soft robotics. Mater. Today 47:187–205
     [Google Scholar]
  153. 153.
    Bellman R. 1957. Dynamic Programming Princeton, NJ: Princeton Univ. Press
  154. 154.
    Eiben AE, Smith J. 2015. From evolutionary computation to the evolution of things. Nature 521:476–82
     [Google Scholar]
  155. 155.
    Auerbach JE, Bongard JC. 2010. Evolving CPPNs to grow three-dimensional physical structures. Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation627–34. New York: ACM
     [Google Scholar]
  156. 156.
    Cheney N, Bongard J, Lipson H. 2015. Evolving soft robots in tight spaces. Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation935–42. New York: ACM
     [Google Scholar]
  157. 157.
    Stanley KO. 2007. Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8:131–62
     [Google Scholar]
/content/journals/10.1146/annurev-control-063022-094301
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On the Timescales of Embodied Intelligence for Autonomous Adaptive Systems
Annual Review of Control, Robotics, and Autonomous Systems 6, 95 (2023); https://doi.org/10.1146/annurev-control-063022-094301
/content/journals/10.1146/annurev-control-063022-094301
/content/journals/10.1146/annurev-control-063022-094301
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