Abstract
Human and animal behavior can be amazingly flexible and adaptive. Even when only considering the dexterity of human arm movements, a rather complex control architecture appears necessary. This control architecture faces three particular challenges, which we discuss in detail. First, sensory redundancy requires the flexible consideration, combination, and integration of different sources of information about the state of the arm and the surrounding environment. Second, motor redundancy requires the flexible consideration and resolution of behavioral alternatives. Third, the continuous uncertainty about body and environment requires flexible control strategies that take these uncertainties into account. Research in cognitive modeling as well as in psychology and neuroscience suggests that the human control system effectively solves and even partially exploits these challenges to generate the observable dexterity. Besides theoretical considerations from control and cognitive modeling perspectives, we survey the capabilities and current drawbacks of the sensorimotor redundancy resolving architecture (SURE_REACH) of human arm reaching. Moreover, we consider an even more modular model of human motor control, which is currently being developed. Both architectures can yield the dexterous behavioral control observable in humans, but only the latter scales to many degrees of freedom. Thus, the architectures may provide insights on how dexterous motor control is realized in humans and on how more adaptive and flexible robot control systems may be developed in the future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamovich, S. V., Levin, M. F., Feldman, A. G. (1997). Central modifications of reflex parameters may underlie the fastest arm movements. Neurophysiology, 77(3), 1460–1469.
Barsalou, L. W., Breazeal, C., Smith, L. B. (2007). Cognition as coordinated non-cognition. Cognitive Processing, 8(1), 79–91. doi:10.1007/s10339-007-0163-1.
Battaglia-Mayer, A., Caminiti, R., Lacquaniti, F., Zago, M. (2003). Multiple levels of representation of reaching in the parieto-frontal network. Cerebral Cortex, 13(10), 1009.
Bernier, P. M., Gauthier, G. M., Blouin, J. (2007). Evidence for distinct, differentially adaptable sensorimotor transformations for reaches to visual and proprioceptive targets. Journal of Neurophysiology, 98(3), 1815.
Bernstein, N. A. (1967). The co-ordination and regulation of movements. Oxford: Pergamon.
Birbaumer, N., & Schmidt, R. (1996). Biologische Psychologie [Biological Psychology], 3rd edn. Berlin: Springer.
Bizzi, E., Polit, A., Morasso, P. (1976). Mechanisms underlying achievement of final head position. Journal of Neurophysiology, 39(2), 435–444.
Botvinick, M., & Cohen, J. (1998). Rubber hands ‘feel’ touch that eyes see. Nature, 391, 756.
Buneo, C., Jarvis, M., Batista, A., Andersen, R. (2002). Direct visuomotor transformations for reaching. Nature, 416(6881), 632–636.
Butz, M. V., Herbort, O., Hoffmann, J. (2007). Exploiting redundancy for flexible behavior: unsupervised learning in a modular sensorimotor control architecture. Psychological Review, 114, 1015–1046.
Butz, M. V., & Pedersen, G. K. M. (2009). The scared robot: motivations in a simulated robot arm. 32nd Annual Conference on Artificial Intelligence, KI 2009, 460–467.
Caggiano, V., Fogassi, L., Rizzolatti, G., Thier, P., Casile, A. (2009). Mirror neurons differentially encode the peripersonal and extrapersonal space of monkeys. Science, 324, 403–406.
Calinon, S., & Billard, A. (2009). Statistical learning by imitation of competing constraints in joint space and task space. Advanced Robotics, 23(15), 2059–2076.
Cruse, H. (2003). The evolution of cognition—a hypothesis. Cognitve Science, 27, 135–155.
de Vignemont, F., Majid, A., Jola, C., Haggard, P. (2009). Segmenting the body into parts: evidence from biases in tactile perception. The Quarterly Journal of Experimental Psychology, 62(3), 500–512.
Doya, K. (1999). What are the computations of the cerebellum, the basal ganglia and the cerebral cortex? Neural Networks, 12(7–8), 961–974.
Doya, K., Ishii, S., Pouget, A., Rao, R. P. N. (2007). Bayesian brain: probabilistic approaches to neural coding. Cambridge: MIT.
Ehrenfeld, S., & Butz, M. V. (2011). A modular, redundant, multi-frame of reference representation for kinematic chains. In IEEE International Conference on Robotics and Automation (pp. 141–147).
Ehrenfeld, S., & Butz, M. V. (2013). The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion. Biological Cybernetics, 107, 61–82. doi:10.1007/s00422-012-0526-2.
Elsner, B., & Hommel, B. (2001). Effect anticipation and action control. Journal of Experimental Psychology: Human Perception and Performance, 27, 229–240.
Engelbrecht, S. E. (2001). Minimum principles in motor control. Journal of Mathematical Psychology, 45, 497–542.
Feldman, A. G. (1966). Functional tuning of nervous system with control of movement or maintenance of a steady posture. II. Controlable parameters of the muscle. Biophysics, 11, 565–578.
Feldman, A. G., & Levin, M. F. (1995). Positional frames of reference in motor control: origin and use. Behavioral and Brain Sciences, 18, 723–806.
Fischer, M. H., Rosenbau, D. A., Vaughan, J. (1997). Speed and sequential effects in reaching. Journal of Experimental Psychology: Human Perception and Performance, 23(2), 404–428.
Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental PSychology, 74, 381–391.
Flash, T., & Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. The Journal of Neuroscience, 5(7), 1688–1703.
Gallese, V., & Goldman, A. (1998). Mirror neurons and the simulation theory of mind-reading. Trends in Cognitive Sciences, 2, 493–501.
Gentner, R., & Classen, J. (2006). Modular organization of finger movements by the human central nervous system. Neuron, 52, 731–42.
Graziano, M. S. A. (2006). The organization of behavioral repertoire in motor cortex. Annual Review of Neuroscience, 29, 105–134.
Graziano, M. S. A., & Cooke, D. F. (2006). Parieto-frontal interactions, personal space, and defensive behavior. Neuropsychologia, 44, 845–859.
Greenwald, A. (1970). Sensory feedback mechanisms in performance control: with special reference to the ideo-motor mechanism. Psychological Review, 77, 73–99.
Harris, C. M., & Wolpert, D. M. (1998). Signal-dependent noise determines motor planning. Nature, 394, 780–784.
Haruno, M., Wolpert, D., Kawato, M. (2003). Hierarchical mosaic for movement generation. In International Congress Series (vol. 1250, pp. 575–590). Amsterdam: Elsevier.
Herbort, O., & Butz, M. V. (2007). Encoding complete body models enables task dependent optimal behavior. In Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, 12–17 August 2007 (pp. 1424–1429).
Herbort, O., & Butz, M. V. (2010). Planning and control of hand orientation in grasping movements. Experimental Brain Research, 202, 867–878.
Herbort, O., & Butz, M. V. (2012). The continuous end-state comfort effect: weighted integration of multiple biases. Psychological research, 76, 345–363. doi:10.1007/s00426-011-0334-7.
Herbort, O., & Butz, M. V. (2011). Habitual and goal-directed factors in (everyday) object handling. Experimental Brain Research, 213, 371–382. doi:10.1007/s00221-011-2787-8.
Herbort, O., Butz, M. V., Hoffmann, J. (2008). Multimodal goal representations and feedback in hierarchical motor control. In Proceedings of the International Conference on Cognitive Systems 2008.
Herbort, O., Butz, M. V., Pedersen, G. K. M. (2010). The SURE_REACH model for motor learning and control of a redundant arm: from modeling human behavior to applications in robotics. In O. Sigaud & J. Peters (Eds.), From motor learning to interaction learning in robots (pp. 85–106). Berlin: Springer.
Herbort, O., Ognibene, D., Butz, M. V., Baldassarre, G. (2007). Learning to select targets within targets in reaching tasks. In 6th IEEE international conference on development and learning, ICDL 2007 (pp. 7–12).
Hof, A. L. (2003). Muscle mechanics and neuromuscular control. Journal of Biomechanics, 36, 1031–1038.
Hoffmann, H., & Möller, R. (2003). Unsupervised learning of a kinematic arm model. In O. Kaynak, E. Alpaydin, E. Oja, L. Xu (Eds.), Artificial neural networks and neural information processing—ICANN/ICONIP 2003. LNCS (vol. 2714, pp. 463–470). Berlin: Springer.
Hoffmann, J. (1993). Vorhersage und Erkenntnis: Die Funktion von Antizipationen in der menschlichen Verhaltenssteuerung und Wahrnehmung. [Anticipation and cognition: The function of anticipations in human behavioral control and perception.]. Göttingen: Hogrefe.
Hoffmann, J., Berner, M., Butz, M. V., Herbort, O., Kiesel, A., Kunde, W., Lenhard, A. (2007a). Explorations of anticipatory behavioral control (ABC): a report from the cognitive psychology unit of the University of Würzburg. Cognitive Processing, 8, 133–142.
Hoffmann, J., Butz, M., Herbort, O., Kiesel, A., Lenhard, A. (2007b). Spekulationen zur strukturideo-motorischer beziehungen. Zeitschrift für Sportpsychologie, 14(3), 95–103.
Hoffmann, M., Marques, H., Arieta, A., Sumioka, H., Lungarella, M., Pfeifer, R. (2010). Body schema in robotics: a review. IEEE Transactions on Autonomous Mental Development, 2, 304 – 324.
Imamizu, H., Kuroda, T., Miyauchi, S., Yoshioka, T., Kawato, M. (2003). Modular organization of internal models of tools in the human cerebellum. PNAS, 100(9), 5461–5466.
Jacob, P., & Jeannerod, M. (2005). The motor theory of social cognition: a critique. Trends in Cognitive Sciences, 9(1), 21–25.
Keysers, C., & Gazzola, V. (2007). Integrating simulation and theory of mind: from self to social cogniton. Trends in Cognitive Sciences, 11(5), 194–196.
Knill, D. C., & Pouget, A. (2004). The bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719.
Koechlin, E., & Summerfield, C. (2007). An information theoretical approach to prefrontal executive function. Trends in cognitive sciences, 11, 229–235. doi:http://dx.doi.org/10.1016/j.tics.2007.04.005.
Konczak, J., & Dichgans, J. (1997). The development toward stereotypic arm kinematics during reaching in the first 3 years of life. Experimental Brain Research, 117, 346–354.
Körding, K. P., pi Ku, S., Wolpert, D. M. (2004). Bayesian integration in force estimation. Journal of Neurophysiology, 92, 3161–3165.
Körding, K. P., & Wolpert, D. M. (2004). Bayesian integration in sensorimotor learning. Nature, 427, 244–247.
Körding, K. P., & Wolpert, D. M. (2006). Bayesian decision theory in sensorimotor control. Trends in Cognitive Sciences, 10(7), 319–326. Special issue: Probabilistic models of cognition.
Latash, M. L., Scholz, J. P., Schöner, G. (2007). Toward a new theory of motor synergies. Motor Control, 11, 276–308.
Latash, M. L., & Turvey, M. T. (Eds.), (1996). Dexterity and its development. Hove: Psychology.
Loeb, G. E., Brown, I. E., Cheng, E. J. (1999). A hierarchical foundation for models of sensorimotor control. Experimental Brain Research, 126, 1–18.
Mussa-Ivaldi, F. A., & Bizzi, E. (2000). Motor learning through the combination of primitives. Philosophical Transactions of the Royal Society: Biological Sciences, 355, 1755–1769.
Mussa-Ivaldi, F. A., Giszter, S. F., Bizzi, E. (1994). Linear combinations of primitives in vertebrate motor control. Proceedings of the National Academy of Sciences, 91, 7534–7538.
Nozaki, D., Kurtzer, I., Scott, S. H. (2006). Limited transfer of learning between unimanual and bimanual skills within the same limb. Nature Neuroscience, 9(10), 1–3. Retrived October 11, 2006 from http://www.nature.com/neuro/journal/vaop/ncurrent/pdf/nn1785.pdf.
Polit, A., & Bizzi, E. (1979). Characteristics of motor programs underlying arm movements in monkeys. Journal of Neurophysiol, 42, 183–194.
Pouget, A., & Snyder, L. H. (2000). Computational approaches to sensorimotor transformations. Nature Neuroscience, 3, 1192–1198.
Rieser, J., Pick Jr, H., Ashmead, D., Garing, A. (1995). Calibration of human locomotion and models of perceptual-motor organization. Journal of Experimental Psychology, 21(3), 480–497.
Rosenbaum, D. A. (2008). Reaching while walking: reaching distance costs more than walking distance. Psychonomic Bulletin and Review, 15(6), 1100–1104.
Rosenbaum, D. A., Inhoff, A. W., Gordon, A. M. (1984). Choosing between movement sequences: a hierarchical editor models. Journal of Experimental Psychology: General, 113(3), 372–393.
Rosenbaum, D. A., Kenny, S. B., Derr, M. A. (1983). Hierarchical control of rapid movement sequences. Journal of Experimental Psychology: Human Perception and Performance, 9(1), 86–102.
Rosenbaum, D. A., Loukopoulos, L. D., Meulenbroek, R. G. J., Vaughan, J., Engelbrecht, S. E. (1995). Planning reaches by evaluating stored postures. Psychological Review, 102(1), 28–67.
Rosenbaum, D. A., Marchak, F., Barnes, H. J., than Vaughan, J., Siotta, J. D., and Jorgensen, M. J. (1990). Constraints for action selection: overhand versus underhand grips. In M. Jeannerod (Ed.), Attention and performance (vol. XIII, pp. 321–345). Hillsdale, New Jersey, Hove and London: Lawrence Erlbaum Associates.
Rosenbaum, D. A., Slotta, J. D., Vaughan, J., Plamondon, R. (1991). Optimal movement selection. Psychological Science, 2, 86–91.
Rosenbaum, D. A., van Heugten, C. M., Caldwell, G. E. (1996). From cognition to biomechanics and back: The end-state comfort effect and the middle-is-faster effect. Acta Psychologica, 94, 59–85.
Roy, D., yuh Hsiao, K., Mavridis, N., Gorniak, P. (2006). Ripley, hand me the cup: sensorimotor representations for grounding word meaning. In International Conference of Automatic Speech Recognition and Understanding.
Sarlegna, F. (2007). Influence of feedback modality on sensorimotor adaptation: Contribution of visual, kinesthetic, and verbal cues. Journal of Motor Behavior, 39(4), 247–258.
Saxe, R. (2005). Against simulation: the argument from error. Trends in Cognitive Sciences, 9(4), 174–179.
Schack, T., & Mechsner, F. (2006). Representation of motor skills in human long-term memory. Neuroscience Letters, 391, 77–81.
Schubotz, R. I. (2007). Prediction of external events with our motor system: towards a new framework. Trends in Cognitive Sciences, 11, 211–218.
Schwartz, A. B., Moran, D. W., Reina, G. A. (2004). Differential representation of perception and action in the frontal cortex. Science, 303, 380–383.
Serwe, S., Drewing, K., Trommershuser, J. (2009). Combination of noisy directional visual and proprioceptive information. Journal of Vision, 9, 1–14.
Shadmehr, R., & Krakauer, J. W. (2008). A computational neuroanatomy for motor control. Experimental Brain Research, 185(3), 359–381.
Shadmehr, R., & Wise, S. P. (2005). The Computational Neurobiology of Reaching and Pointing: A foundation for motor learning. Cambridge: MIT.
Soechting, J. F., Buneo, C. A., Herrmann, U., Flanders, M. (1995). Moving effortlessly in three dimensions: does Donders’ law apply to arm movement? Journal of Neuroscience, 15, 6271–6280.
Tong, C., & Flanagan, J. R. (2003). Task-specific internal models for kinematic transformations. Journal of Neurophysiology, 90, 578–585.
Trommershäuser, J., Maloney, L. T., Landy, M. S. (2003). Statistical decision theory and the selection of rapid, goal-directed movements. Journal of the Optical Society of America A, 20, 1419–1433.
van Beers, R., Haggard, P., Wolpert, D. (2004). The role of execution noise in movement variability. Journal of Neurophysiology, 91(2), 1050.
Van Hedel, H. J. A., Biedermann, M., Erni, T., Dietz, V. (2002). Obstacle avoidance during human walking: transfer of motor skill from one leg to the other. The Journal of Physiology, 543(2), 709.
Vijayakumar, S., Toussaint, M., Petkos, G., Howard, M. (2009). Planning and moving in dynamic environments. In B. Sendhoff, E. Körner, O. Sporns, H. Ritter, & K. Doya, Creating Brain-Like Intelligence. Lecture Notes in Computer Science (Vol. 5436, pp. 151–191). Berlin: Springer. doi:10.1007/978-3-642-00616-6_9.
von Hofsten, C. (1980). Predictive reaching for moving objects by human infants* 1. Journal of Experimental Child Psychology, 30(3), 369–382.
von Holst, E., & Mittelstaedt, H. (1950). Das Reafferenzprinzip. Naturwissenschaften, 37, 464–476.
Welch, G., & Bishop, G. (1995). An introduction to the Kalman filter. Technical Report TR 95-041, University of North Carolina at Chapel Hill, Department of Computer Science.
Wells, J., Hyler-Both, D., Danley, T., Wallace, G. (2002). Biomechanics of growth and development in the healthy human infant: a pilot study. JAOA: Journal of the American Osteopathic Association, 102(6), 313.
Wolpert, D. M., Doya, K., Kawato, M. (2003). A unifying computational framework for motor control and social interaction. Philosophical Transactions of the Royal Society of London, 358, 593–602.
Wolpert, D. M., & Ghahramani, Z. (2000). Computational principles of movement neuroscience. Nature Neuroscience, 3, 1212–1217.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Ehrenfeld, S., Herbort, O., Butz, M.V. (2013). Modular, Multimodal Arm Control Models. In: Baldassarre, G., Mirolli, M. (eds) Computational and Robotic Models of the Hierarchical Organization of Behavior. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39875-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-39875-9_7
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39874-2
Online ISBN: 978-3-642-39875-9
eBook Packages: Computer ScienceComputer Science (R0)