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Neurorobotics: From Vision to Action

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Springer Handbook of Robotics

Abstract

The lay view of a robot is a mechanical human, and thus robotics has always been inspired by attempts to emulate biology. In this Chapter, we extend this biological motivation from humans to animals more generally, but with a focus on the central nervous systems rather than the bodies of these creatures. In particular, we investigate the sensorimotor loop in the execution of sophisticated behavior. Some of these sections concentrate on cases where vision provides key sensory data. Neuroethology is the study of the brain mechanisms underlying animal behavior, and Sect. 62.2 exemplifies the lessons it has to offer robotics by looking at optic flow in bees, visually guided behavior in frogs, and navigation in rats, turning then to the coordination of behaviors and the role of attention. Brains are composed of diverse subsystems, many of which are relevant to robotics, but we have chosen just two regions of the mammalian brain for detailed analysis. Section 62.3 presents the cerebellum. While we can plan and execute actions without a cerebellum, the actions are no longer graceful and become uncoordinated. We reveal how a cerebellum can provide a key ingredient in an adaptive control system, tuning parameters both within and between motor schemas. Section 62.4 turns to the mirror system, which provides shared representations which bridge between the execution of an action and the observation of that action when performed by others. We develop a neurobiological model of how learning may forge mirror neurons for hand

movements, provide a Bayesian view of a robot mirror system, and discuss what must be added to a mirror system to support robot imitation. We conclude by emphasizing that, while neuroscience can inspire novel robotic designs, it is also the case that robots can be used as embodied test beds for the analysis of brain models.

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Abbreviations

AIP:

anterior interparietal area

APG:

adjustable pattern generator

CE:

computer ethics

CF:

climbing fibers

CF:

contact formation

CMAC:

cerebellar model articulation controller

EM:

expectation maximization

FARS:

Fagg-Arbib-Rizzolatti-Sakata

IT:

inferotemporal

IT:

intrinsic tactile

MF:

Mossy fibers

MNS:

mirror neuron system

MPFIM:

multiple paired forward-inverse models

PB:

parametric bias

PC:

Purkinje cells

PC:

principal contact

PF:

parallel fibers

PFC:

prefrontal cortex

PID:

proportional–integral–derivative

RNNPB:

recurrent neural network with parametric bias

SMA:

shape-memory alloy

STS:

superior temporal sulcus

TMS:

transcranial magnetic stimulation

VRML:

virtual reality modeling language

WG:

world graph

WTA:

winner-take-all

References

  1. W.G. Walter: The Living Brain (Duckworth, London 1953), reprinted by Pelican Books, Harmondsworth, 1961

    Google Scholar 

  2. V. Braitenberg: Vehicles: Experiments in Synthetic Psychology (Bradford Books/The MIT Press, Canbridge 1984)

    Google Scholar 

  3. I. Segev, M. London: Dendritic Processing. In: The Handbook of Brain Theory and Neural Networks, ed. by M.A. Arbib (Bradford Books/The MIT Press, Cambridge 2003) pp. 324–332, Second Edition

    Google Scholar 

  4. Y. Fregnac: Hebbian synaptic plasticity. In: The Handbook of Brain Theory and Neural Networks, ed. by M.A. Arbib (Bradford Books/The MIT Press, Cambridge 2003) pp. 515–522, 2nd Edition

    Google Scholar 

  5. R.D. Beer: Intelligence as Adaptive Behavior: An Experiment in Computational Neuroethology (Academic, San Diego 1990)

    MATH  Google Scholar 

  6. D. Cliff: Neuroethology, computational. In: The Handbook of Brain Theory and Neural Networks, ed. by M.A. Arbib (Bradford Books/The MIT Press, Cambridge 2002), 2nd Edition

    Google Scholar 

  7. W. Reichardt: Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In: Sensory Communication, ed. by W.A. Rosenblith (MIT Press and Wiley, New York, London 1961) pp. 303–317

    Google Scholar 

  8. A. Borst, M. Dickinson: Visual course control in flies. In: The Handbook of Brain Theory and Neural Networks, ed. by M.A. Arbib (Bradford Books/The MIT Press, Cambridge 2003) pp. 1205–1210, 2nd edn.

    Google Scholar 

  9. P. van der Smagt, F. Groen: Visual feedback in motion. In: Neural Systems for Robotics, ed. by O. Omidvar, P. van der Smagt (Morgan Kaufmann, San Francisco 1997) pp. 37–73

    Google Scholar 

  10. P. van der Smagt: Teaching a robot to see how it moves. In: Neural Network Perspectives on Cognition and Adaptive Robotics, ed. by A. Browne (Institute of Physics Publishing, Bristol 1997) pp. 195–219

    Google Scholar 

  11. S.C. Liu, A. Usseglio-Viretta: Fly-like visuomotor responses of a robot using aVLSI motion-sensitive chips, Biol. Cybern. 85(6), 449–457 (2001)

    Article  Google Scholar 

  12. F. Ruffier, S. Viollet, S. Amic, N. Franceschini: Bio-inspired optical flow circuits for the visual guidance of micro air vehicles, Inter. Symp. Circuits Syst. (ISCAS) 2003, Vol. 3 (2003)

    Google Scholar 

  13. M.B. Reiser, M.H. Dickinson: A test bed for insect-inspired robotic control, Philos. Trans. Math. Phys. Eng. Sci. 361(1811), 2267–2285 (2003)

    Article  MathSciNet  Google Scholar 

  14. M.V. Srinivasan, S. Zhang, J.S. Chahl: Landing strategies in honeybees, and possible applications to autonomous airborne vehicles, Biol. Bull. 200(2), 216–221 (2001)

    Article  Google Scholar 

  15. A. Barron, M.V. Srinivasan: Visual regulation of ground speed and headwind compensation in freely flying honey bees. Apis mellifera L, J. Exp. Biol. 209(Pt 5), 978–984 (2006)

    Article  Google Scholar 

  16. T. Vladusich, J.M. Hemmi, M.V. Srinivasan, J. Zeil: Interactions of visual odometry and landmark guidance during food search in honeybees, J. Exp. Biol. 208, 4123–4135 (2005)

    Article  Google Scholar 

  17. M.V. Srinivasan, S.W. Zhang: Visual control of honeybee flight, EXS 84, 95–113 (1997)

    Google Scholar 

  18. B. Webb: Can robots make good models of biological behaviour?, Behav. Brain Sci. 24, 1033–1094 (2001)

    Google Scholar 

  19. J.Y. Lettvin, H. Maturana, W.S. McCulloch, W.H. Pitts: What the frogʼs eye tells the frog brain, Proc. IRE, Vol. 47 (1959) pp. 1940–1951

    Google Scholar 

  20. D. Ingle: Visual releasers of prey catching behavior in frogs and toads, Brain Behav. Evol. 1, 500–518 (1968)

    Article  Google Scholar 

  21. R.L. Didday: A model of visuomotor mechanisms in the frog optic tectum, Math. Biosci. 30, 169–180 (1976)

    Article  Google Scholar 

  22. M.A. Arbib: Levels of modeling of visually guided behavior, Behav. Brain Sci. 10, 407–465 (1987)

    Article  Google Scholar 

  23. M.A. Arbib: Visuomotor coordination: neural models and perceptual robotics. In: Visuomotor Coordination: Amphibians, Comparisons, Models, and Robots, ed. by J.P. Ewert, M.A. Arbib (Plenum, New York 1989) pp. 121–171

    Google Scholar 

  24. T. Collett: Do toads plan routes? A study of detour behavior of B. viridis, J. Compar. Physiol. 146, 261–271 (1982)

    Article  Google Scholar 

  25. M.A. Arbib, D.H. House: Depth and detours: An essay on visually guided behavior. In: Vision, Brain and Cooperative Computation ed. by M.A. Arbib, A.R. Hanson (Bradford Books/The MIT Press, Cambridge 1987) pp.129–163

    Google Scholar 

  26. F.J. Corbacho, M.A. Arbib: Learning to detour, Adapt. Behav. 4, 419–468 (1995)

    Article  Google Scholar 

  27. A. Cobas, M.A. Arbib: Prey-catching and predator-avoidance in frog and toad: defining the schemas, J. Theor. Biol 157, 271–304 (1992)

    Article  Google Scholar 

  28. R.C. Arkin: Motor schema-based mobile robot navigation, Int. J. Robot. Res. 8, 92–112 (1989)

    Article  MathSciNet  Google Scholar 

  29. O. Khatib: Real-time obstacle avoidance for manipulators and mobile robots, Int. J. Robot. Res. 5, 90–98 (1986)

    Article  Google Scholar 

  30. B.H. Krogh, C.E. Thorpe: Integrated path planning and dynamic steering control for autonomous vehicles, Proc. IEEE Int. Conf. Robot. Autom. (San Francisco 1986) pp. 1664–1669

    Google Scholar 

  31. R.A. Brooks, C.L. Breazeal, M. Marjanoviæ, B. Scassellati: The COG project: building a humanoid robot. In: Computation for Metaphor, Analogy and Agents, Vol. 1562, ed. by C.L. Nehaniv (Springer, New York 1999) pp. 52–87

    Chapter  Google Scholar 

  32. R.C. Arkin: Behavior-Based Robotics (MIT Press, Cambridge 1998)

    Google Scholar 

  33. R.C. Arkin, M. Fujita, T. Takagi, R. Hasegawa: An ethological and emotional basis for human-Rrbot interaction, Robot. Auton. Syst. 42(3-4), 191–201 (2003)

    Article  MATH  Google Scholar 

  34. P. Dean, P. Redgrave, G.W.M. Westby: Event or emergency? Two response systems in the mammalian superior colliculus, Trends Neurosci. 12, 138–147 (1989)

    Article  Google Scholar 

  35. B.E. Stein, M.A. Meredith: The Merging of the Senses (MIT Press, Cambridge 1993)

    Google Scholar 

  36. T. Strosslin, C. Krebser, A. Arleo, W. Gerstner: Combining multimodal sensory input for spatial learning, Artificial Neural Networks – ICANN 2002, Lecture Notes Comput. Sci. 2415, 87–92 (2002)

    Article  Google Scholar 

  37. J. OʼKeefe, L. Nadel: The Hippocampus as a Cogn. Map (Clarendon, Oxford 1978)

    Google Scholar 

  38. V. Braitenberg: Taxis, kinesis, decussation, Progr. Brain Res. 17, 210–222 (1965)

    Article  Google Scholar 

  39. A. Guazzelli, F.J. Corbacho, M. Bota, M.A. Arbib: Affordances, motivation, and the world graph theory, Adapt. Behav. 6, 435–471 (1998)

    Article  Google Scholar 

  40. J.J. Gibson: The senses considered as perceptual systems (Allen and Unwin, London 1966)

    Google Scholar 

  41. S.B. Choi, S.W. Ban, M. Lee: Biologically motivated visual attention system using bottom-up saliency map and top-down inhibition, Neural Inf. Proc. – Lett. Rev. 2(1), 19–25 (2004)

    Google Scholar 

  42. M.A. Arbib: Perceptual structures and distributed motor control. In: Handbook of Physiology – The Nervous System II. Motor Control, ed. by V.B. Brooks (Am. Physiological Society, Bethesda 1981) pp. 1449–1480.

    Google Scholar 

  43. A. Guazelli, F.J. Corbacho, M. Bota, M.A. Arbib: Affordance, motivation, and the world graph theory, Adapt. Behav. 6, 435–471 (1998)

    Article  Google Scholar 

  44. I. Lieblich, M.A. Arbib: Multiple representations of space underlying behavior, Behav. Brain Sci. 5, 627–659 (1982)

    Article  Google Scholar 

  45. B. Girard, D. Filliat, J.A. Meyer, A. Berthoz, A. Guillot: Integration of navigation and action selection functionalities in a computational model of cortico-basal-ganglia-thalamo-cortical loops, Adapt. Behav. 13(2), 115–130 (2005)

    Article  Google Scholar 

  46. J.A. Meyer, A. Guillot, B. Girard, M. Khamassi, P. Pirim, A. Berthoz: The Psikharpax project: towards building an artificial rat, Robot. Auton. Syst. 50(4), 211–223 (2005)

    Article  Google Scholar 

  47. D.M. Lyons, M.A. Arbib: A formal model of computation for sensory-based robotics, IEEE Trans. Robot. Autom. 5, 280–293 (1989)

    Article  Google Scholar 

  48. G. Metta, P. Fitzpatrick, L. Natale: YARP: yet another robot platform, Int. J. Adv. Robot. Syst. 3(1), 43–48 (2006)

    Google Scholar 

  49. L.G. Ungerleider, M. Mishkin: Two cortical visual systems. In: Analysis of Visual Behavior, ed. by D.J. Ingle, M.A. Goodale, R.J.W. Mansfield (MIT Press, Cambridge 1982) pp. 549–586

    Google Scholar 

  50. M.A. Goodale, A.D. Milner: Separate visual pathways for perception and action, Trends Neurosci. 15, 20–25 (1992)

    Article  Google Scholar 

  51. M. Jeannerod, B. Biguer: Visuomotor mechanisms in reaching within extra-personal space, In: Advances in the Analysis of Visual Behavior, ed. by D.J. Ingle, R.J.W. Mansfield, M.A. Goodale (MIT Press, 1982) pp. 387–409 pp. 285-306

    Google Scholar 

  52. B. Hoff, M.A. Arbib: Simulation of interaction of hand transport and preshape during visually guided reaching to perturbed targets, J. Motor Behav. 25, 175–192 (1993)

    Article  Google Scholar 

  53. B. Hoff, M.A. Arbib: A model of the effects of speed, accuracy, and perturbation on visually guided reaching. In: Control of Arm Movement in Space: Neurophysiological and Computational Approaches, Experimental Brain Research Series, Vol. 22, ed. by R. Caminiti, P.B. Johnson, Y. Burnod (Springerg, New York 1992)

    Google Scholar 

  54. R.C. Miall, D.J. Weir, D.M. Wolpert, J.F. Stein: Is the cerebellum a Smith predictor?, J. Motor Behav. 25, 203–216 (1993)

    Article  Google Scholar 

  55. G. Deco, E.T. Rolls: Attention and working memory: a dynamical model of neuronal activity in the prefrontal cortex, Eur. J. Neurosci. 18(8), 2374–2390 (2003)

    Article  Google Scholar 

  56. L. Itti, C. Koch: A saliency-based search mechanism for overt and covert shifts of visual attention, Vision Res. 40, 1489–1506 (2000)

    Article  Google Scholar 

  57. J.M. Wolfe: Guided search 2.0: a revised model of visual search, Psychonomic Bull. Rev. 1, 202–238 (1994)

    Article  Google Scholar 

  58. A. Yarbus: Eye Movements and Vision (Plenum, New York 1967)

    Google Scholar 

  59. V. Navalpakkam, L. Itti: Modeling the influence of task on attention, Vision Res. 45, 205–231 (2005)

    Article  Google Scholar 

  60. F. Orabona, G. Metta, G. Sandini: Object-based visual attention: a model for a behaving robot, Conf. Comput. Vision Pattern Recognition (2005) pp. 89–89

    Google Scholar 

  61. G. Sandini, V. Tagliasco: An anthropomorphic retina-like structure for scene analysis, Comput. Vision, Graphics Image Proc. 14(3), 365–372 (1980)

    Article  Google Scholar 

  62. G. Rizzolatti, L. Riggio, I. Dascola, C. Umiltá: Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention, Neuropsychologia 25, 31–40 (1987)

    Article  Google Scholar 

  63. J.R. Flanagan, R.S. Johansson: Action plans used in action observation, Nature 424, 769–771 (2003)

    Article  Google Scholar 

  64. J.R. Flanagan, P. Vetter, R.S. Johansson, D.M. Wolpert: Prediction precedes control in motor learning, Curr. Biol. 13, 146–150 (2003)

    Article  Google Scholar 

  65. J.M. Mataric, M. Pomplun: Fixation behavior in observation and imitation of human movement, Brain Res. Cogn. Brain Res. 7, 191–202 (1998)

    Article  Google Scholar 

  66. J.C. Eccles, M. Ito, J. Szentágothai: The Cerebellum as a Neuronal Machine (Springer, New York 1967)

    Google Scholar 

  67. M. Ito: Cerebellar circuitry as a neuronal machine, Progr. Neurobiol. 78(4), 272–303 (2006)

    Article  Google Scholar 

  68. D.A. Marr: A theory of cerebellar cortex, J. Physiol. 202, 437–470 (1969)

    Google Scholar 

  69. J.S. Albus: A theory of cerebellar function, Math. Biosci. 10, 25–61 (1971)

    Article  Google Scholar 

  70. J.S. Albus: Data storage in the cerebellar model articulation controller (CMAC), J. Dyn. Syst. Meas. Contr. ASME 3, 228–233 (1975)

    Article  Google Scholar 

  71. W.T. Miller: Real-time application of neural networks for sensor-based control of robots with vision, IEEE Trans. Syst. Man Cybern. 19, 825–831 (1994)

    Article  Google Scholar 

  72. J. Peters, P. van der Smagt: Searching a scalable approach to cerebellar based control, Appl. Intell. 17, 11–33 (2002)

    Article  MATH  Google Scholar 

  73. E.J. Nijhof, E. Kouwenhoven: Simulation of multijoint arm movements. In: Biomechanics and Neural Control of Posture and Movement, ed. by J.M. Winters, P.E. Crago (Springer, New York 2002) pp. 363–372

    Google Scholar 

  74. M. Damsgaard, J. Rasmussen, S.T. Christensen: Numerical simulation and justification of antagonists in isometric squatting, Proceedings of the 12th Conference of the European Society of Biomechanics, ed. by P.J. Prendergast, T.C. Lee, A.J. Carr (Royal Academy of Medicine in Ireland, 2000)

    Google Scholar 

  75. D. Bullock, J. Contreras-Vidal: How spinal neural networks reduce discrepancies between motor intention and motor realization. In: Variability and Motor Control, ed. by K. Newell, D. Corcos (Human Kinetics, Champaign 1993) pp. 183–221

    Google Scholar 

  76. S. Schaal, N. Schweighofer: Computational motor control in humans and robots, Curr. Opin. Neurobiol. 15, 675–682 (2005)

    Article  Google Scholar 

  77. P. van der Smagt, G. Hirzinger: The cerebellum as computed torque model, Fourth Int. Conf. Knowledge-Based Intell. Eng. Syst. Allied Technol., Vol. 2 (2000) pp. 760–763

    Google Scholar 

  78. M. Ebadzadeh, B. Tondu, C. Darlot: Computation of inverse functions in a model of cerebellar and reflex pathways allow to control a mobile mechanical segment, Neuroscience 133, 29–49 (2005)

    Article  Google Scholar 

  79. M. Ito: The Cerebellum and Neural Control (Raven, New York 1984)

    Google Scholar 

  80. P. van der Smagt: Cerebellar control of robot arms, Connect. Sci. 10, 301–320 (1998)

    Article  Google Scholar 

  81. P. van der Smagt: Benchmarking cerebellar control, Robot. Auton. Syst. 32, 237–251 (2000)

    Article  Google Scholar 

  82. C. Sabourin, O. Bruneau: Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks, Robot. Auton. Syst. 51, 81–99 (2005)

    Article  Google Scholar 

  83. J.C. Houk, J.T. Buckingham, A.G. Barto: Models of the cerebellum and motor learning, Behav. Brain Sci. 19(3), 368–383 (1996)

    Article  Google Scholar 

  84. M.A. Arbib, C.C. Boylls, P. Dev: Neural models of spatial perception and the control of movement. In: Kybernetik und Bionik/Cybernetics (Oldenbourg, 1974) pp. 216–231

    Google Scholar 

  85. N. Schweighofer: Computational Models of the Cerebellum in the Adaptive Control of Movements (University of Southern California, Los Angeles 1995), Ph.D. thesis

    Google Scholar 

  86. N. Schweighofer, M.A. Arbib, M. Kawato: Role of the cerebellum in reaching quickly and accurately:I. A functional anatomical model of dynamics control, Eur. J. Neurosci. 10, 86–94 (1998)

    Article  Google Scholar 

  87. N. Schweighofer, J. Spoelstra, M.A. Arbib, M. Kawato: Role of the cerebellum in reaching quickly and accurately: II. A neural model of the intermediate cerebellum, Eur. J. Neurosci. 10, 95–105 (1998)

    Article  Google Scholar 

  88. M.A. Arbib, N. Schweighofer, W.T. Thach: Modeling the cerebellum: from adaptation to coordination. In: Motor Control and Sensory-Motor Integration: Issues and Directions, ed. by D.J. Glencross, J.P. Piek (North-Holland Elsevier Science, Amsterdam 1995) pp. 11–36

    Chapter  Google Scholar 

  89. D. Wolpert, M. Kawato: Multiple paired forward and inverse models for motor control, Neural Networks 11, 1317–1329 (1998)

    Article  Google Scholar 

  90. P. van der Smagt, F. Groen, K. Schulten: Analysis and control of a rubbertuator robot arm, Biol. Cybern. 75(4), 433–440 (1996)

    Google Scholar 

  91. R. Osu, H. Gomi: Multijoint muscle regulation mechanisms examined by measured human arm stiffness and EMG signals, J. Neurophysiol. 81(4), 1458–1468 (1999)

    Google Scholar 

  92. G. Rizzolatti, L. Fadiga, V. Gallese, L. Fogassi: Premotor cortex and the recognition of motor actions, Cogn. Brain Res. 3, 131–141 (1995)

    Article  Google Scholar 

  93. V. Gallese, L. Fadiga, L. Fogassi, G. Rizzolatti: Action recognition in the premotor cortex, Brain 119, 593–609 (1996)

    Article  Google Scholar 

  94. S.T. Grafton, M.A. Arbib, L. Fadiga, G. Rizzolatti: Localization of grasp representations in humans by PET: 2. Observation compared with imagination, Exp. Brain Res. 112, 103–111 (1996)

    Article  Google Scholar 

  95. G. Rizzolatti, L. Fadiga, M. Matelli, V. Bettinardi, D. Perani, F. Fazio: Localization of grasp representations in humans by positron emission tomography: 1. Observation versus execution, Exp. Brain Res. 111, 246–252 (1996)

    Article  Google Scholar 

  96. L. Fadiga, G. Buccino, L. Craighero, L. Fogassi, V. Gallese, G. Pavesi: Corticospinal excitability is specifically modulated by motor imagery: a magnetic stimulation study, Neuropsychologia 37, 147–158 (1999)

    Article  Google Scholar 

  97. B. Voelkl, L. Huber: Imitation as faithful copying of a novel technique in marmoset monkeys, PLoS ONE 2(7), e611 (2007)

    Article  Google Scholar 

  98. R.W. Byrne: Imitation as behavior parsing, Philos. Trans. R. Soc. London 358, 529–536 (2003)

    Article  Google Scholar 

  99. M. Iacoboni, R.P. Woods, M. Brass, H. Bekkering, J.C. Mazziotta, G. Rizzolatti: Cortical mechanisms of human imitation, Science 286, 2526–2528 (1999)

    Article  Google Scholar 

  100. M.A. Arbib, G. Rizzolatti: Neural expectations: a possible evolutionary path from manual skills to language, Commun. Cognition 29, 393–424 (1997)

    Google Scholar 

  101. M.A. Arbib: From monkey-like action recognition to human language: an evolutionary framework for neurolinguistics (with commentaries and authorʼs response), Behav. Brain Sci. 28, 105–167 (2005)

    Google Scholar 

  102. A.M. Liberman, F.S. Cooper, D.P. Shankweiler, M. Studdert-Kennedy: Perception of the speech code, Psychol. Rev. 74, 431–461 (1967)

    Article  Google Scholar 

  103. G. Rizzolatti, G. Luppino, M. Matelli: The organization of the cortical motor system: new concepts, Electroencephalogr. Clin. Neurophysiol. 106, 283–296 (1998)

    Article  Google Scholar 

  104. G. Rizzolatti, L. Fogassi, V. Gallese: Neurophysiological mechanisms underlying the understanding and imitation of action, Nat. Rev. Neurosc. 2, 661–670 (2001)

    Article  Google Scholar 

  105. M.A. Umiltà, E. Kohler, V. Gallese, L. Fogassi, L. Fadiga, C. Keysers, G. Rizzolatti: I know what you are doing. A neurophysiological study, Neuron 31(1), 155–165 (2001)

    Article  Google Scholar 

  106. P.F. Ferrari, S. Rozzi, L. Fogassi: Mirror neurons responding to observation of actions made with tools in monkey ventral premotor cortex, J. Cogn. Neurosci. 17, 212–226 (2005)

    Article  Google Scholar 

  107. L. Fogassi, P.F. Ferrari, B. Gesierich, S. Rozzi, F. Chersi, G. Rizzolatti: Parietal Lobe: From action organization to intention understanding, Science 308(4), 662–667 (2005)

    Article  Google Scholar 

  108. M. Taira, S. Mine, A.P. Georgopoulos, A. Murata, H. Sakata: Parietal cortex neurons of the monkey related to the visual guidance of hand movement, Exp. Brain Res. 83, 29–36 (1990)

    Article  Google Scholar 

  109. A.H. Fagg, M.A. Arbib: Modeling parietal-premotor interactions in primate control of grasping, Neural Netw. 11, 1277–1303 (1998)

    Article  Google Scholar 

  110. G. Rizzolatti, G. Luppino: Grasping movements: visuomotor transformations. In: The Handbook of Brain Theory and Neural Networks, ed. by M.A. Arbib (MIT Press, Cambridge 2003) pp. 501–504, 2nd ed

    Google Scholar 

  111. S.H. Johnson-Frey: The neural bases of complex tool use in humans, Trends Cogn. Sci. 8, 71–78 (2004)

    Article  Google Scholar 

  112. E. Oztop, M.A. Arbib: Schema design and implementation of the grasp-related mirror neuron system, Biol. Cybern. 87(2), 116–140 (2002)

    Article  MATH  Google Scholar 

  113. D.I. Perrett, J.K. Hietanen, M.W. Oram, P.J. Benson: Organization and functions of cells in the macaque temporal cortex, Philos. Trans. Royal Society London, B 335, 23–50 (1992)

    Google Scholar 

  114. D.P. Carey, D.I. Perrett, M.W. Oram: Recognizing, understanding and producing action. In: Handbook of Neuropsychology: Action and Cognition, Vol. 11, ed. by M. Jeannerod, J. Grafman (Elsevier, Amsterdam 1997) pp. 111–130

    Google Scholar 

  115. B. Bonaiuto, E. Rosta, M.A. Arbib: Extending the mirror neuron system model, I. Audible actions and invisible grasps, Biol. Cybern. 96(1), 9–38 (2006)

    Article  MathSciNet  Google Scholar 

  116. D.M. Wolpert, Z. Ghahramani, R.J. Flanagan: Perspectives and problems in motor learning, Cogn. Sci. 5(11), 487–494 (2001)

    Google Scholar 

  117. M.I. Jordan, D.E. Rumelhart: Forward models: supervised learning with a distal teacher, Cogn. Sci. 16(3), 307–354 (2006)

    Google Scholar 

  118. M. Kawato, K. Furukawa, R. Suzuki: A hierarchical neural network model for control and learning of voluntary movement, Biol. Cybern. 57, 169–185 (1987)

    Article  MATH  Google Scholar 

  119. Y. Demiris, M.H. Johnson: Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning, Connect. Sci. 15(4), 231–243 (2003)

    Article  Google Scholar 

  120. M. Haruno, D.M. Wolpert, M. Kawato: MOSAIC model for sensorimotor learning and control, Neural Comput. 13, 2201–2220 (2001)

    Article  MATH  Google Scholar 

  121. E. Oztop, D.M. Wolpert, M. Kawato: Mental state inference using visual control parameters, Cogn. Brain Res. 22, 129–151 (2005)

    Article  Google Scholar 

  122. A.L. Woodward: Infant selectively encode the goal object of an actorʼs reach, Cognition 69, 1–34 (1998)

    Article  Google Scholar 

  123. R.O. Duda, P.E. Hart, D.G. Stork: Pattern Classification, 2nd edn. (Wiley, New York 2001)

    MATH  Google Scholar 

  124. G. Metta, G. Sandini, L. Natale, L. Craighero, L. Fadiga: Understanding mirror neurons: a bio-robotic approach, Interaction Studies 7(2), 197–232 (2006), special issue on Epigenetic Robotics

    Article  Google Scholar 

  125. M. Lopaes, J. Santos–Victor: Visual learning by imitation with motor representations, IEEE Trans. Syst. Man Cybern, Part B Cybern. 35(3), 438–449 (2005)

    Article  Google Scholar 

  126. P. Fitzpatrick, G. Metta: Grounding vision through experimental manipulation, Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 361(1811), 2165–2185 (2003)

    Article  MathSciNet  Google Scholar 

  127. P. Fitzpatrick: First contact: an active vision approach to segmentation, IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS) (Las Vegas 2003)

    Google Scholar 

  128. H. Sakata, M. Taira, M. Kusunoki, A. Murata, Y. Tanaka: The TINS lecture – The parietal association cortex in depth perception and visual control of action, Trends Neurosci. 20(8), 350–358 (1997)

    Article  Google Scholar 

  129. M.A. Arbib, A. Billard, M. Iacoboni, E. Oztop: Synthetic brain imaging: grasping, mirror neurons and imitation, Neural Netw. 13(8-9), 975–997 (2000)

    Article  Google Scholar 

  130. S. Schaal, A.J. Ijspeert, A. Billard: Computational approaches to motor learning by imitation, Philos. Trans. R. Soc. Biol. Sci. 358(1431), 537–547 (2003)

    Article  Google Scholar 

  131. E. Oztop, M. Kawato, M.A. Arbib: Mirror neurons and imitation: a computationally guided review, Neural Netw. 19(3), 254–271 (2006)

    Article  MATH  Google Scholar 

  132. J. Tani, M. Ito, Y. Sugita: Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB, Neural Netw. 17(8-9), 1273–1289 (2004)

    Article  Google Scholar 

  133. Y. Kuniyoshi, Y. Yorozu, M. Inaba, H. Inoue: From visuomotor self learning to visual imitation – a neural architecture for humanoid learning, IEEE Int. Conf. Robot. Autom. (ICRA), Vol. 3 (2003) pp. 3132–3139

    Google Scholar 

  134. D.M. Wolpert, K. Doya, M. Kawato: A unifying computational framework for motor control and social interaction, Phil. Trans. R. Soc. Biol. Sci. 358(1431), 593–602 (2003)

    Article  Google Scholar 

  135. C.L. Nehaniv: Nine billion correspondence problems. In: Imitation and Social Learning in Robots, Humans, and Animals: Behavioural, Social and Communicative Dimensions, ed. by C.L. Nehaniv, K. Dautenhahn (Cambridge Univ. Press, Cambridge 2006)

    Google Scholar 

  136. C.L. Nehaniv, K. Dautenhahn: Mapping between dissimilar bodies: affordances and the algebraic foundations of imitation, Eur. Workshop Learning Robots (EWRL-7) (Edinburgh 1998)

    Google Scholar 

  137. Y. Demiris, G. Hayes: Imitation as a dual-route process featuring predictive and learning components: a biologically-plausible computational model. In: Imitation in Animals and Artifacts, ed. by K. Dautenhahn, C. Nehaniv (MIT Press, Cambridge 2002)

    Google Scholar 

  138. M. Ito, K. Noda, Y. Hoshino, J. Tani: Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model,

    Google Scholar 

  139. E.L. Sauser, A. Billard: Parallel and distributed neural models of the ideomotor principle: An investigation of imitative cortical pathways, Neural Netw. 19, 285–298 (2006)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Computer, Neuroscience and USC Brain Project, University of Southern California, 90089-2520, Los Angeles, CA, USA

    Michael A. Arbib Prof

  2. Department of Robotics, Brain and Cognitive Sciences, Italian Institute of Technology, Via Morego 30, 16163, Genova, Italy

    Giorgio Metta Prof

  3. Institut für Robotik und Mechatronik, Deutsches Zentrum für Luft- und Raumfahrt (DLR) Oberpfaffenhofen, Münchner Str. 20, 82230, Wessling, Germany

    Patrick van der Smagt

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  1. Michael A. Arbib Prof
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  2. Giorgio Metta Prof
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  3. Patrick van der Smagt
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Correspondence to Michael A. Arbib Prof , Giorgio Metta Prof or Patrick van der Smagt .

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Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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Arbib, M.A., Metta, G., van der Smagt, P. (2008). Neurorobotics: From Vision to Action. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_63

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_63

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