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Artificial Intelligence: Computational Approach to Vision and Motor Control

Ellen C. Hildreth, John M. Hollerbach

10.1002/cphy.cp010515

Source: Supplement 5: Handbook of Physiology, The Nervous System, Higher Functions of the Brain

Originally published: 1987

Published online: January 2011

Full Article on Wiley Online Library



Abstract

The sections in this article are:

1 Founding Principles of Artificial Intelligence
2 Computational Approach to Neuroscience
3 Relation to Other Areas of Artificial Intelligence
3.1 Study of Vision
3.2 Representational Structure of Vision
3.3 Natural Constraints in Vision
3.4 From Theory to Implementation: Detection of Intensity Changes
4 Study of Motor Control
4.1 Features of Motor Control Research
4.2 Natural Constraints in Motor Control
4.3 Movement‐Planning Hierarchy
4.4 Biological Implications
5 Conclusions
Figure 1. Figure 1.

Light intensities measured by digital camera of the rectangular area outlined in the image in A are shown in B.
Figure 2. Figure 2.

Derivation of 3‐D structure from 2‐D motion. A: 3 views of 3‐D wire‐frame object that is rotating about a central vertical axis. B: projected 2‐D image and motion of object. Arrows represent projected 2‐D velocity of individual points on object.
Figure 3. Figure 3.

Ambiguity of interpreting structure from motion. A: set of dots on surface of rotating transparent cylinder are projected onto 2‐D display screen. B: bird's‐eye view of projection of dots in A. C: field of randomly moving dots that project to same 2‐D image as dots shown in B.
Figure 4. Figure 4.

Aperture problem. A: motion detector that views moving edge E through limited aperture A detects only component of motion c in direction perpendicular to edge. B: circle undergoing pure translation to the right. Arrows along contour represent perpendicular components of velocity obtained from changing image. C: contour C undergoes translation, rotation, and deformation to yield contour C' at some later time. The true motion of point p is ambiguous.
Figure 5. Figure 5.

Motion illusions. A, C, E: true velocity fields for logarithmic spiral, ellipse, and deformed circle, respectively, rotating about their centers. Short line segments along smooth contours represent direction and speed of movement of individual points on contours. B, D, F: smoothest velocity fields consistent with rotating patterns shown in A, C, E.
Figure 6. Figure 6.

Analog models of velocity field computation. A: simple resistive network that computes smoothest velocity field. Conductances g and g and currents Ii represent properties of moving contour measured directly from image. The 2‐D velocity field along contour is represented implicitly by combination of inputs and resulting voltages Vi. B: hypothetical neural implementation of circuit shown in A Synaptic‐mediated currents Ii and additional inputs Ri represent properties of moving contour. Resulting voltages Vi, sampled by dendrodendritic synapses, together with input currents represent local velocities along contour.
Figure 7. Figure 7.

Detection of intensity changes. A: 1‐D intensity profile represents light intensities measured along horizontal line of natural image. B: result of smoothing intensity profile shown in A. C: first derivative of smoothed intensity profile shown in B. D: second derivative of smoothed intensity profile shown in B. Dashed lines show relationship between significant changes in B, peaks in C, and zero‐crossings in D.
Figure 8. Figure 8.

Receptive fields of retinal ganglion cells. A: shape of spatial receptive fields of retinal ganglion cells, described quantitatively as difference of 2 Gaussian functions: a narrow positive one and broader negative one. B: on‐ and off‐center cells, which respond in opposite manner to light stimulation in central and surrounding areas of receptive fields.
Figure 9. Figure 9.

Detection of intensity changes. A: image of natural scene. B: result of filtering image shown in A with difference‐of‐Gaussians function. C: positions of zero‐crossings of filtered image shown in B.
Figure 10. Figure 10.

Use of multiple operator sizes. A: image of natural scene. B, C, D: positions of zero‐crossings that result from filtering the image shown in A with difference‐of‐Gaussian functions whose central positive region has diameter of 6, 12, and 24 image elements, respectively.
Figure 11. Figure 11.

Simple cell models. A: simple cell model proposed by Marr and Hildreth 124 in which responses of adjacent on‐ and off‐center lateral geniculate nucleus (LGN) cells are combined through AND‐like operation. B: simple cell model proposed by Poggio 146 in which adjacent LGN cells of same type are combined through AND‐NOT operation.
Figure 12. Figure 12.

A: “staircase” stimulus used by Richter and Ullman 160 consisting of adjacent bars of different intensities. B: graphs represent cross section of intensity distribution across bar pattern, for range of separations between two step changes of intensity. C: asymmetric difference‐of‐Gaussians function. D: results of filtering patterns shown in B through difference‐of‐Gaussians function shown in C.
Figure 13. Figure 13.

Test of the zero‐crossing hypothesis. A: staircase stimulus of Fig. 12, with contrast inverted so that step changes of intensity are light at left and dark at right. B: result of filtering the profile in A with a difference‐of‐Gaussians function of intermediate size.
Figure 14. Figure 14.

Modular planning and control structure for robot arm movement. Trajectory is planned in hand coordinates, synthesizing a hybrid force‐position strategy. End‐point trajectory x(t) is transformed into joint trajectory θ(t) by solving inverse kinematics. The feedforward torques T(t) are found by solving inverse dynamics and corrected by feedback for force and position errors.
Figure 15. Figure 15.

Different planning variables and their resultant trajectories for planar two‐joint arm movement. A: straight line in joint coordinates generates complex curved end‐point trajectory. B: straight line in Cartesian coordinates requires relatively complex elbow and shoulder joint movement.
Figure 16. Figure 16.

Joint‐angle plots of shoulder angle θ1 versus elbow angle θ2 (A) and corresponding end‐point trajectories (B) for perfect straight‐line Cartesian trajectories (solid lines) versus staggered‐joint interpolation (dotted lines).
Figure 17. Figure 17.

Trajectories of unrestrained arm movement between vertical‐plane targets measured with Selspot system. End‐point trajectories are shown in A‐D as projected onto vertical plane, and corresponding joint‐angle plots of elbow versus shoulder angle are shown in E‐H.
Figure 18. Figure 18.

Simulations of vertical planar arm movements involving shoulder and elbow joints. A: ratio of elbow velocity to shoulder velocity is shown for each arm movement. Contour lines of constant joint‐rate ratio are imposed on movement plane in B. B: arm is shown in starting position in lower right quadrant of movement plane. Center represents shoulder point, and l1 and l2 are upper arm and forearm lengths. Outer circle represents workspace boundary, points of maximal reach. Simulated movements begin from starting position and approach different points on boundary along straight‐line paths.
Figure 19. Figure 19.

A: tangential velocity profiles of wrist point for 6 vertical arm movements measured with Selspot system. B: movements are normalized for time and distance to demonstrate underlying invariance in profile shape. C: hypothetical phantom arm carrying load and superimposed on actual arm allows movement speed and load conditions to be simply changed if and only if tangential velocity profile is invariant.


Figure 1.

Light intensities measured by digital camera of the rectangular area outlined in the image in A are shown in B.



Figure 2.

Derivation of 3‐D structure from 2‐D motion. A: 3 views of 3‐D wire‐frame object that is rotating about a central vertical axis. B: projected 2‐D image and motion of object. Arrows represent projected 2‐D velocity of individual points on object.



Figure 3.

Ambiguity of interpreting structure from motion. A: set of dots on surface of rotating transparent cylinder are projected onto 2‐D display screen. B: bird's‐eye view of projection of dots in A. C: field of randomly moving dots that project to same 2‐D image as dots shown in B.



Figure 4.

Aperture problem. A: motion detector that views moving edge E through limited aperture A detects only component of motion c in direction perpendicular to edge. B: circle undergoing pure translation to the right. Arrows along contour represent perpendicular components of velocity obtained from changing image. C: contour C undergoes translation, rotation, and deformation to yield contour C' at some later time. The true motion of point p is ambiguous.



Figure 5.

Motion illusions. A, C, E: true velocity fields for logarithmic spiral, ellipse, and deformed circle, respectively, rotating about their centers. Short line segments along smooth contours represent direction and speed of movement of individual points on contours. B, D, F: smoothest velocity fields consistent with rotating patterns shown in A, C, E.



Figure 6.

Analog models of velocity field computation. A: simple resistive network that computes smoothest velocity field. Conductances g and g and currents Ii represent properties of moving contour measured directly from image. The 2‐D velocity field along contour is represented implicitly by combination of inputs and resulting voltages Vi. B: hypothetical neural implementation of circuit shown in A Synaptic‐mediated currents Ii and additional inputs Ri represent properties of moving contour. Resulting voltages Vi, sampled by dendrodendritic synapses, together with input currents represent local velocities along contour.



Figure 7.

Detection of intensity changes. A: 1‐D intensity profile represents light intensities measured along horizontal line of natural image. B: result of smoothing intensity profile shown in A. C: first derivative of smoothed intensity profile shown in B. D: second derivative of smoothed intensity profile shown in B. Dashed lines show relationship between significant changes in B, peaks in C, and zero‐crossings in D.



Figure 8.

Receptive fields of retinal ganglion cells. A: shape of spatial receptive fields of retinal ganglion cells, described quantitatively as difference of 2 Gaussian functions: a narrow positive one and broader negative one. B: on‐ and off‐center cells, which respond in opposite manner to light stimulation in central and surrounding areas of receptive fields.



Figure 9.

Detection of intensity changes. A: image of natural scene. B: result of filtering image shown in A with difference‐of‐Gaussians function. C: positions of zero‐crossings of filtered image shown in B.



Figure 10.

Use of multiple operator sizes. A: image of natural scene. B, C, D: positions of zero‐crossings that result from filtering the image shown in A with difference‐of‐Gaussian functions whose central positive region has diameter of 6, 12, and 24 image elements, respectively.



Figure 11.

Simple cell models. A: simple cell model proposed by Marr and Hildreth 124 in which responses of adjacent on‐ and off‐center lateral geniculate nucleus (LGN) cells are combined through AND‐like operation. B: simple cell model proposed by Poggio 146 in which adjacent LGN cells of same type are combined through AND‐NOT operation.



Figure 12.

A: “staircase” stimulus used by Richter and Ullman 160 consisting of adjacent bars of different intensities. B: graphs represent cross section of intensity distribution across bar pattern, for range of separations between two step changes of intensity. C: asymmetric difference‐of‐Gaussians function. D: results of filtering patterns shown in B through difference‐of‐Gaussians function shown in C.



Figure 13.

Test of the zero‐crossing hypothesis. A: staircase stimulus of Fig. 12, with contrast inverted so that step changes of intensity are light at left and dark at right. B: result of filtering the profile in A with a difference‐of‐Gaussians function of intermediate size.



Figure 14.

Modular planning and control structure for robot arm movement. Trajectory is planned in hand coordinates, synthesizing a hybrid force‐position strategy. End‐point trajectory x(t) is transformed into joint trajectory θ(t) by solving inverse kinematics. The feedforward torques T(t) are found by solving inverse dynamics and corrected by feedback for force and position errors.



Figure 15.

Different planning variables and their resultant trajectories for planar two‐joint arm movement. A: straight line in joint coordinates generates complex curved end‐point trajectory. B: straight line in Cartesian coordinates requires relatively complex elbow and shoulder joint movement.



Figure 16.

Joint‐angle plots of shoulder angle θ1 versus elbow angle θ2 (A) and corresponding end‐point trajectories (B) for perfect straight‐line Cartesian trajectories (solid lines) versus staggered‐joint interpolation (dotted lines).



Figure 17.

Trajectories of unrestrained arm movement between vertical‐plane targets measured with Selspot system. End‐point trajectories are shown in A‐D as projected onto vertical plane, and corresponding joint‐angle plots of elbow versus shoulder angle are shown in E‐H.



Figure 18.

Simulations of vertical planar arm movements involving shoulder and elbow joints. A: ratio of elbow velocity to shoulder velocity is shown for each arm movement. Contour lines of constant joint‐rate ratio are imposed on movement plane in B. B: arm is shown in starting position in lower right quadrant of movement plane. Center represents shoulder point, and l1 and l2 are upper arm and forearm lengths. Outer circle represents workspace boundary, points of maximal reach. Simulated movements begin from starting position and approach different points on boundary along straight‐line paths.



Figure 19.

A: tangential velocity profiles of wrist point for 6 vertical arm movements measured with Selspot system. B: movements are normalized for time and distance to demonstrate underlying invariance in profile shape. C: hypothetical phantom arm carrying load and superimposed on actual arm allows movement speed and load conditions to be simply changed if and only if tangential velocity profile is invariant.

References
 1. Abbs, J. H., and V. L. Gracco. Sensorimotor actions in the control of multimovement speech gestures. Trends Neurosci. 6: 391–395, 1983.
 2. Abbs, J. H., V. L. Gracco, and K. J. Cole. Control of multimovement coordination: sensorimotor mechanisms in speech motor programming. J. Mot. Behav. 16: 195–231, 1984.
 3. Abend, W., E. Bizzi, and P. Morasso. Human arm trajectory formation. Brain 105: 331–348, 1982.
 4. Adelson, E. H., and J. A. Movshon. Phenomenal coherence of moving visual patterns, Nature Lond. 300: 523–525, 1982.
 5. Albus, J. S. A theory of cerebellar functions. Math. Biosci. 10: 25–61, 1971.
 6. Albus, J. S. Data storage in the cerebellar model articulation controller (CMAC). ASME J. Dyn. Syst. Meas. Control 97: 228–233, 1975.
 7. Albus, J. S. A new approach to manipulator control: the cerebellar model articulation controller (CMAC). ASME J. Dyn. Syst. Meas. Control 97: 270–277, 1975.
 8. Albus, J. S. Brains, Behavior, and Robotics. Peterborough, NH: Byte Books, 1981.
 9. An, C., C. G. Atkeson, and J. M. Hollerbach. Estimation of inertial parameters of rigid body links of manipulators. Proc. IEEE Conf. Decision and Control, Miami, FL, December 1985, p. 990–995.
 10. Anstis, S. M. The perception of apparent motion. Philos. Trans. R. Soc. Lond. B Biol. Sci. 290: 153–168, 1980.
 11. Arbib, M. A., and S.‐I. Amari. Sensorimotor transformations in the brain (with a critique of the tensor theory of cerebellum). J. Theor. Biol. 112: 123–155, 1985.
 12. Arimoto, S., S. Kawamura, and F. Miyazaki. Can mechanical robots learn by themselves? In: Robotics Research: The Second International Symposium, edited by H. Hanafusa and H. Inoue. Cambridge, MA: MIT Press, 1985, p. 127–134.
 13. Atkeson, C. G., C. An, and J. M. Hollerbach. Rigid body load identification for manipulators. Proc. IEEE Conf. Decision and Control, Miami, FL, December 1985, p. 996–1002.
 14. Atkeson, C. G., and J. M. Hollerbach. Kinematic features of unrestrained vertical arm movements. J. Neurosci. 5: 2318–2330, 1985.
 15. Ballard, D. H. Cortical connections and parallel processing: structure and function. Behav. Brain Sci. 9: 67–120, 1986.
 16. Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1: 371–394, 1972.
 17. Barrow, H. G., and J. M. Tenenbaum. Recovering intrinsic scene characteristics from images. In: Computer Vision Systems, edited by A. R. Hanson and E. M. Riseman. New York: Academic, 1978, p. 3–26.
 18. Bishop, P. O., J. S. Coombs, and G. H. Henry. Responses to visual contours: spatio‐temporal aspects of excitation in the receptive fields of simple striate neurones. J. Physiol. Lond. 219: 625–657, 1971.
 19. Bizzi, E., N. Accornero, W. Chapple, and N. Hogan. Posture control and trajectory formation during arm movement. J. Neurosci. 4: 2738–2745, 1984.
 20. Bizzi, E., W. Chapple, and N. Hogan. Mechanical properties of muscles: implications for motor control. Trends Neurosci. 5: 395–398, 1982.
 21. Bizzi, E., P. Dev, P. Morasso, and A. Polit. Effect of load disturbances during centrally initiated movements. J. Neurophysiol. 41: 542–556, 1978.
 22. Blakemore, C., and F. W. Campbell. On the existence of neurones in the human visual system selectively sensitive to the orientation and size of retinal images. J. Physiol. Lond. 203: 237–260, 1969.
 23. Bobick, A. A hybrid approach to structure from motion. Proc. ACM Interdisciplinary Workshop on Motion: Representation and Perception, Toronto, April 1983, p. 91–109.
 24. Börjesson, E., and C. Von Hofsten. Visual perception of motion in depth: application of a vector model to three‐dot motion patterns. Percept. Psychophys. 13: 169–179, 1973.
 25. Boycott, B. B., and H. Wassle. The morphological types of ganglion cells of the domestic cat's retina. J. Physiol. Lond. 240: 397–419, 1974.
 26. Brady, J. M., J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason (editors). Robot Motion: Planning and Control. Cambridge, MA: MIT Press, 1982.
 27. Braunstein, M. L. Depth perception in rotation dot patterns: effects of numerosity and perspective. J. Exp. Psychol. 64: 415–420, 1962.
 28. Braunstein, M. L. Depth Perception Through Motion. New York: Academic, 1976.
 29. Breitmeyer, B. G., and L. Ganz. Temporal studies with flashed gratings: inferences about human transient and sustained channels. Vision Res. 17: 861–866, 1977.
 30. Burt, P., and G. Sperling. Time, distance, and feature trade‐offs in visual apparent motion. Psychol. Rev. 88: 171–195, 1981.
 31. Campbell, F. W., and J. G. Robson. Applications of Fourier analysis to the visibility of gratings. J. Physiol. Lond. 197: 551–556, 1968.
 32. Canny, J. F. Finding Edges and Lines in Images. Cambridge, MA: MIT Artif. Intell. Lab., June 1983. (Tech. Rep. 720.)
 33. Chomsky, N. Aspects of the Theory of Syntax. Cambridge, MA: MIT Press, 1965.
 34. Cleland, B. G., M. W. Dubin, and W. R. Levick. Sustained and transient neurons in the cat's retina and LGN. J. Physiol. Lond. 217: 473–496, 1971.
 35. Cleland, B. G., W. R. Levick, and K. J. Sanderson. Properties of sustained and transient ganglion cells in the cat retina. J. Physiol. Lond. 228: 649–680, 1973.
 36. Clocksin, W. F. Perception of surface slant and edge labels from optical flow: a computational approach. Perception 9: 253–269, 1980.
 37. Cornsweet, T. N. Visual Perception. New York: Academic, 1970.
 38. Cowan, J. D. Some remarks on channel bandwidths for visual contrast, detection. Neurosci. Res. Program Bull. 15: 492–517, 1977.
 39. Craig, J. J. Adaptive control of manipulators through repeated trials. Proc. Am. Control Conf. San Diego, June 1984, p. 1566–1574.
 40. Crossman, E. R., and P. J. Goodeve. Feedback control of hand‐movement and Fitts' law. Q. J. Exp. Psychol. A Hum. Exp. Psychol. 35A: 251–278, 1983.
 41. Cynader, M., and D. Regan. Neurones in cat parastriate cortex sensitive to the direction of motion in three‐dimensional space. J. Physiol. Lond. 274: 548–569, 1978.
 42. Cynader, M., and D. Regan. Neurons in cat visual cortex tuned to the direction of motion in depth: effect of positional disparity. Vision Res. 22: 967–982, 1982.
 43. Davis, L. A survey of edge detection techniques. Comput. Graph. Image Proc. 4: 248–270, 1975.
 44. Delatizky, J. Final position control in simulated planar arm movements. Soc. Neurosci. Abstr. 8: 283, 1982.
 45. De Monasterio, F. M. Properties of concentrically organized X and Y ganglion cells of macaque retina. J. Neurophysiol. 41: 1394–1417, 1978.
 46. De Monasterio, F. M. Center and surround mechanisms of opponent‐color X and Y ganglion cells of retina of macaques. J. Neurophysiol. 41: 1418–1434, 1978.
 47. De Monasterio, F. M., and P. Gouras. Functional properties of ganglion cells of the rhesus monkey retina. J. Physiol. Lond. 251: 167–195, 1975.
 48. Dreher, B., and K. J. Sanderson. Receptive field analysis: responses to moving visual contours by single lateral geniculate neurons in the cat. J. Physiol. Lond. 234: 95–118, 1973.
 49. Enroth‐Cugell, C., and J. G. Robson. The contrast sensitivity of retinal ganglion cells of the cat. J. Physiol. Lond. 187: 517–522, 1966.
 50. Feldman, A. G. Change of muscle length due to shift of the equilibrium point of the muscle‐load system. Biofizika 19: 534–538, 1974.
 51. Feldman, A. G. Control of muscle length. Biofizika 19: 749–753, 1974.
 52. Fennema, C. I., and W. B. Thompson. Velocity determination in scenes containing several moving objects. Comput. Graph. Image Proc. 9: 301–315, 1979.
 53. Fetters, L., and J. Delatizky. Quantitative analysis of infant arm movements. Soc. Neurosci. Abstr. 10: 339, 1984.
 54. Flash, T. Organizing Principles Underlying the Formation of Hand Trajectories. Cambridge, MA: Harvard/MIT Div. Health Sci. Tech., 1983. PhD thesis.
 55. Flash, T., and S. A. Mussa‐Ivaldi. Inferring movement and muscle synergies from multi‐joint arm posture. Soc. Neurosci. Abstr. 10: 635, 1984.
 56. Gibson, J. J. The Perception of the Visual World. Boston, MA: Houghton Mifflin, 1950.
 57. Gibson, J. J. The Senses Considered as Perceptual Systems. Boston, MA: Houghton Mifflin, 1966.
 58. Gibson, J. J. The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin, 1979.
 59. Gibson, J. J., and E. J. Gibson. Continuous perceptive transformations and the perception of rigid motion. J. Exp. Psychol. 54: 129–138, 1957.
 60. Goodwin, A. W., G. H. Henry, and P. O. Bishop. Direction selectivity of simple striate cells: properties and mechanisms. J. Neurophysiol. 38: 1500–1523, 1975.
 61. Graham, N. Visual detection of aperiodic spatial stimuli by probability summation among narrow band channels. Vision Res. 17: 637–652, 1977.
 62. Graham, N., and J. Nachmias. Detection of grating patterns containing two spatial frequencies: a comparison of single‐channel and multiple‐channel models. Vision Res. 11: 251–259, 1971.
 63. Green, B. F. Figure coherence in the kinetic depth effect. J. Exp. Psychol. 62: 272–282, 1961.
 64. Grillner, S. Locomotion in vertebrates: central mechanisms and reflex interaction. Physiol. Rev. 55: 247–304, 1975.
 65. Grzywacz, N., and E. Hildreth. The Incremental Rigidity Scheme for Recovering Structure from Motion: Position vs. Velocity Based Formulations. Cambridge, MA: MIT Artif. Intell. Lab., August 1985. (AI Memo 845.)
 66. Hildreth, E. C. The detection of intensity changes by computer and biological vision systems. Comput. Vision Graph. Image Proc. 22: 1–27, 1983.
 67. Hildreth, E. C. The Measurement of Visual Motion. Cambridge, MA: MIT Press, 1984.
 68. Hochstein, S., and R. M. Shapley. Quantitative analysis of retinal ganglion cell classifications. J. Physiol. Lond. 262: 237–264, 1976.
 69. Hochstein, S., and H. Spitzer. Zero‐crossing detectors in primary visual cortex? Biol. Cybern. 51: 195–199, 1984.
 70. Hoffer, J. A., M. J. O'Donovan, C. A. Pratt, and G. E. Loeb. Discharge patterns of hindlimb motoneurons during normal cat locomotion. Science Wash. DC 213: 466–468, 1981.
 71. Hoffman, D. D., and B. E. Flinchbaugh. The interpretation of biological motion. Biol. Cybern. 42: 195–204, 1982.
 72. Hoffman, K.‐P., J. Stone, and S. M. Sherman. Relay of receptive field properties in dorsal lateral geniculate nucleus of the cat. J. Neurophysiol. 35: 518–531, 1972.
 73. Hogan, N. Mechanical impedance control in assistive devices and manipulators. In: Robot Motion: Planning and Control, edited by J. M. Brody, J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason. Cambridge, MA: MIT Press, 1982, p. 361–371.
 74. Hogan, N. Impedance control of industrial robots. Robotics Comput. Integrated Manufacturing 1: 97–113, 1984.
 75. Holland, H. C. The Spiral After‐Effect. Oxford, UK: Pergamon, 1965.
 76. Hollerbach, J. M. A recursion Lagrangian formulation of manipulator dynamics and a comparative study of dynamics formulation complexity. IEEE Trans. Syst. Man Cybern. 10: 730–736, 1980.
 77. Hollerbach, J. M. Computers, brains, and the control of movement. Trends Neurosci. 5: 189–192, 1982.
 78. Hollerbach, J. M. Optimum kinematic design for a seven degree of freedom manipulator. In: Robotics Research: The Second International Symposium, edited by H. Hanafusa and H. Inoue. Cambridge, MA: MIT Press, 1985, p. 215–222.
 79. Hollerbach, J. M., and C. G. Atkeson. Characterization of joint‐interpolated arm movements. In: Generation and Modulation of Action Patterns, edited by H. Heuer and C. Fromm. New York: Springer‐Verlag, in press.
 80. Hollerbach, J. M., and T. Flash. Dynamic interactions between limb segments during planar arm movement. Biol. Cybern. 44: 67–77, 1982.
 81. Hollerbach, J. M., S. P. Moore, and C. G. Atkeson. Generalized joint interpolation explains straight and curved arm movements. Soc. Neurosci. Abstr. 11: 329, 1985.
 82. Hollerbach, J. M., and G. Sahar. Wrist‐partitioned inverse kinematic accelerations and manipulator dynamics. Int. J. Robotics Res. 2 (4): 61–76, 1983.
 83. Hollerbach, J. M., and K. C. Suh. Redundancy resolution of manipulators through torque minimization. Proc. IEEE Int. Conf. Robotics and Automation, St. Louis, MO, March 1985, p. 1016–1021.
 84. Horn, B. K. P. Understanding image intensities. Artif. Intell. 8: 201–231, 1977.
 85. Horn, B. K. P. Robot Vision. Cambridge, MA: MIT Press, 1985.
 86. Horn, B. K. P., and B. G. Schunck. Determining optical flow. Artif. Intell. 17: 185–203, 1981.
 87. Hubel, D. H., and T. N. Wiesel. Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex. J. Physiol. Lond. 160: 106–154, 1962.
 88. Hubel, D. H., and T. N. Wiesel. Receptive fields and functional architecture of monkey striate cortex. J. Physiol. Lond. 195: 215–243, 1968.
 89. Hulliger, M. The mammalian muscle spindle and its central control. Rev. Physiol. Biochem. Pharmacol. 101: 1–110, 1984.
 90. Ikeda, H., and M. J. Wright. Receptive field organization of “sustained” and “transient” retinal ganglion cells which subserve different functional roles. J. Physiol. Lond. 227: 769–800, 1972.
 91. Ikeda, H., and M. J. Wright. Spatial and temporal properties of “sustained” and “transient” neurons in area 17 of the cat's visual cortex. Exp. Brain Res. 22: 363–383, 1975.
 92. Jacobsen, S. C., J. E. Wood, D. F. Knutti, and K. B. Blggers. The Utah/MIT dexterous hand: work in progress. Int. J. Robotics Res. 3(4): 21–50, 1984.
 93. Jacobsen, S. C., J. E. Wood, D. F. Knutti, K. B. Biggers, and E. K. Iversen. The version I Utah/MIT dextrous hand. In: Robotics Research: The Second International Symposium, edited by H. Hanafusa and H. Inoue. Cambridge, MA: MIT Press, 1985, p. 301–308.
 94. Jansson, G., and G. Johansson. Visual perception of bending motion. Perception 2: 321–326, 1973.
 95. Johansson, G. Perception of motion and changing form. Scand. J. Psychol. 5: 181–208, 1964.
 96. Johansson, G. Visual perception of biological motion and a model for its analysis. Percept. Psychophys. 14: 201–211, 1973.
 97. Johansson, G. Visual motion perception. Sci. Am. 232: 76–88, 1975.
 98. Johansson, G. Visual event perception. In: Handbook of Sensory Physiology. Perception, edited by R. Held, H. W. Liebowitz, and H.‐L. Teuber. Berlin: Springer‐Verlag, 1978, vol. VIII, p. 675–711.
 99. Johnson, T. L. Feedback control. In: Robot Motion: Planning and Control, edited by J. M. Brady, J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason. Cambridge, MA: MIT Press, 1982, p. 127–146.
 100. Kazerooni, H., T. B. Sheridan, and P. K. Houpt. Robust compliant motion for manipulators. Part I: The fundamental concepts of compliant motion. IEEE J. Robotics Autom. RA‐2: 83–92, 1986.
 101. Kearney, R. E., and I. W. Hunter. System identification of human triceps surae stretch reflex dynamics. Exp. Brain Res. 51: 117–127, 1983.
 102. Kelso, J. A. S., and K. G. Holt. Exploring a vibratory systems analysis of human movement production. J. Neurophysiol. 43: 1183–1196, 1980.
 103. Koch, C., and T. Poggio. Biophysics of computational systems: neurons, synapses, and membranes. In: New Insights into Synaptic Function, edited by G. M. Edelman, W. E. Gall, and W. M. Cowan. New York: Wiley, in press.
 104. Koch, C., and T. Poggio. The synaptic veto mechanism: does it underly direction and orientation selectivity in the visual cortex? In: Models of the Visual Cortex, edited by D. Rose and V. Dobson. New York: Wiley, 1985, p. 408–419.
 105. Kuffler, S. W. Discharge patterns and functional organization of mammalian retina. J. Neurophysiol. 16: 37–68, 1953.
 106. Kulikowski, J. J., and D. J. Tolhurst. Psychophysical evidence for sustained and transient channels in human vision. J. Physiol. Lond. 232: 149–163, 1973.
 107. Lacquaniti, F., and J. F. Soechting. Behavior of the stretch reflex in a multi‐jointed limb. Brain Res. 311: 161–166, 1984.
 108. Land, E. H. Color vision and the natural image. Proc. Natl. Acad. Sci. USA 45: 115–129, 1959.
 109. Land, E. H. Experiments in color vision. Sci. Am. 200: 84–99, 1959.
 110. Land, E. H., and J. J. McCann. Lightness and the retinex theory. J. Opt. Soc. Am. 61: 1–11, 1971.
 111. Lappin, J. S., and H. H. Bell. The detection of coherence in moving random dot patterns. Vision Res. 16: 161–168, 1976.
 112. Lathrop, R. L. Parallelism in manipulator dynamics. Int. J. Robotics Res. 4 (2): 80–102, 1985.
 113. Lawton, D. T. Processing translational motion sequences. Comput. Vision Graph. Image Proc. 22: 116–144, 1983.
 114. Liegeois, A. Automatic supervisory control of the configuration and behavior of multibody mechanisms. IEEE Trans. Sys. Man Cybern. 7: 868–871, 1977.
 115. Loeb, G. E. Finding common ground between robotics and physiology. Trends Neurosci. 6: 203–204, 1983.
 116. Loeb, G. E. The control and responses of mammalian muscle spindles during normally executed motor tasks. In: Exercise and Sport Sciences Reviews, edited by R. L. Terjung. Lexington, MA: Collamore, 1984, vol. 12, p. 157–204.
 117. Longuet‐Higgins, H. C. A computer algorithm for reconstructing a scene from two projections. Nature Lond. 293: 133–135, 1981.
 118. Longuet‐Higgins, H. C., and K. Prazdny. The interpretation of moving retinal images. Proc. R. Soc. Lond. B Biol. Sci. 208: 385–397, 1981.
 119. Lozano‐Perez, T. Task Planning. In: Robot Motion: Planning and Control, edited by J. M. Brady, J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason. Cambridge, MA: MIT Press, 1982, p. 473–498.
 120. Luh, J. Y. S., M. W. Walker, and R. P. C. Paul. Resolved‐acceleration control of mechanical manipulators. IEEE Trans. Autom. Control 25: 468–474, 1980.
 121. Marr, D. A theory of cerebellar cortex. J. Physiol. Lond. 202: 437–470, 1969.
 122. Marr, D. Artificial intelligence—a personal view. Artif. Intell. 9: 37–48, 1976.
 123. Marr, D. Vision. San Francisco, CA: Freeman, 1982.
 124. Marr, D., and E. C. Hildreth. Theory of edge detection. Proc. R. Soc. Lond. B Biol. Sci. 207: 187–217, 1980.
 125. Marr, D., and T. Poggio. From understanding computation to understanding neural circuitry. Neurosci. Res. Program Bull. 15: 470–488, 1977.
 126. Marr, D., and T. Poggio. A computational theory of human stereo vision. Proc. R. Soc. Lond. B Biol. Sci. 204: 301–328, 1979.
 127. Marr, D., and S. Ullman. Directional selectivity and its use in early visual processing. Proc. R. Soc. Lond. B Biol. Sci. 211: 151–180, 1981.
 128. Mason, M. T. Compliance. In: Robot Motion: Planning and Control, edited by J. M. Brady, J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason. Cambridge, MA: MIT Press, 1982, p. 305–322.
 129. Mayhew, J. E. W., and J. P. Frisby. Psychophysical and computational studies towards a theory of human stereopsis. Artif. Intell. 17: 349–385, 1981.
 130. McMahon, T. A. Muscles, Reflexes, and Locomotion. Princeton, NJ: Princeton Univ. Press, 1984.
 131. Minsky, M. Semantic Information Processing. Cambridge, MA: MIT Press, 1968.
 132. Minsky, M., and S. Papert. Perceptrons: An Introduction to Computational Geometry. Cambridge, MA: MIT Press, 1969.
 133. Morasso, P. Spatial control of arm movements. Exp. Brain Res. 42: 223–227, 1981.
 134. Morgan, M. J., B. Moulden, G. Mather, and R. J. Watt. Intensity‐response nonlinearities and the theory of edge localization. Vision Res. 24: 713–719, 1984.
 135. Movshon, J. A., E. H. Adelson, M. S. Gizzi, and W. T. Newsome. The analysis of moving visual patterns. In: Pattern Recognition Mechanisms, edited by C. Chagas, R. Gattas, and C. G. Gross. Rome: Vatican Press, 1985, p. 117–151.
 136. Mukerjee, A., and D. H. Ballard. Self‐calibration in robot manipulators. Proc. IEEE Conf. Robotics and Automation, St. Louis, MO, March 1985, p. 1050–1057.
 137. Mussa‐Ivaldi, S. A., N. Hogan, and E. Bizzi. Invariant features of hand postural stiffness. Soc. Neurosci. Abstr. 10: 337, 1984.
 138. Nagel, H.‐H., and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Trans. Patt. Anal. Machine Intell. PAMI‐8: 565–593, 1986.
 139. Olsen, H. B., and G. A. Bekey. Identification of parameters in models of robots with rotary joints. Proc. IEEE Conf. Robotics and Automation, St. Louis, MO, March 1985, p. 1045–1050.
 140. Pantle, A. J., and L. Picciano. A multistable display: evidence for two separate motion systems in human vision. Science Wash. DC 193: 500–502, 1976.
 141. Peichl, L., and H. Wassle. Size, scatter and convergence of ganglion cell receptive field centers in the cat retina. J. Physiol. Lond. 291: 117–141, 1979.
 142. Pettigrew, J. D., T. Nikara, and P. O. Bishop. Responses to moving slits by single units in cat striate cortex. Exp. Brain Res. 6: 373–390, 1968.
 143. Pieper, D. L. The Kinematics of Manipulators Under Computer Control. Stanford, CA: Stanford Univ., 1968. PhD thesis.
 144. Poggio, G. F., and T. Poggio. The analysis of stereopsis. Annu. Rev. Neurosci. 7: 379–412, 1984.
 145. Poggio, G. F., and W. H. Talbot. Mechanisms of static and dynamic stereopsis in foveal cortex of the rhesus monkey. J. Physiol. Lond. 315: 469–492, 1981.
 146. Poggio, T. Visual algorithms. In: Physical and Biological Processing of Images, edited by O. J. Braddick and A. C. Sleigh. Berlin: Springer‐Verlag, 1983, p. 128–153.
 147. Poggio, T., and C. Koch. An Analog Model of Computation for the Ill‐Posed Problems of Early Vision. Cambridge, MA: MIT Artif. Intell. Lab., May 1984. (AI Memo 783.)
 148. Poggio, T., H. Voorhees, and A. Yuille. A Regularized Solution to Edge Detection. Cambridge, MA: MIT Artif. Intell. Lab., May 1985. (AI Memo 833.)
 149. Polit, A., and E. Bizzi. Characteristics of motor programs underlying arm movements in monkeys. J. Neurophysiol. 42: 183–194, 1979.
 150. Pratt, W. Digital Image Processing. New York: Wiley, 1978.
 151. Prazdny, K. Egomotion and relative depth map from optical flow. Biol. Cybern. 36: 87–102, 1980.
 152. Prazdny, K. On the information in optical flows. Comput. Vision Graph. Image Proc. 22: 239–259, 1983.
 153. Raibert, M. H. A model for sensorimotor control and learning. Biol. Cybern. 29: 29–36, 1978.
 154. Raibert, M. H. (editor). Int. J. Robotics Res.: Special Issue on Legged Locomotion. 3 (2): 2–146, 1984.
 155. Raibert, M. H., and J. J. Craig. Hybrid position/force control of manipulators. In: Robot Motion: Planning and Control, edited by J. M. Brady, J. M. Hollerbach, T. L. Johnson, T. Lozano‐Perez, and M. T. Mason. Cambridge, MA: MIT Press, 1982, p. 419–428.
 156. Raibert, M. H., and B. K. P. Horn. Configuration space control. The Industrial Robot p. 69–73, June 1978.
 157. Rajan, V. T. Planning of minimum‐time trajectories for robot arms. Proc. IEEE Int. Conf. Robotics and Automation, St. Louis, MO, March 1985, p. 759–764.
 158. Ratliff, F. Mach Bands: Quantitative Studies on Neural Networks in the Retina. San Francisco, CA.: Holden‐Day, 1965.
 159. Regan, D., K. Beverly, and M. Cynader. The visual perception of motion in depth. Sci. Am. 241: 136–151, 1979.
 160. Richter, J., and S. Ullman. A model for the temporal properties of X‐type and Y‐type receptive fields in the primate retina. Biol. Cybern. 43: 127–145, 1982.
 161. Richter, J., and S. Ullman. Simple cells and the detection of zero crossings. Biol. Cybern. 53: 195–202, 1986.
 162. Robinson, D. A. Models of the saccadic eye movement control system. Kybernetik 14: 71–83, 1973.
 163. Rodieck, R. W. Quantitative analysis of cat retinal ganglion cell responses to visual stimuli. Vision Res. 5: 583–601, 1965.
 164. Rodieck, R. W., and J. Stone. Analysis of receptive fields of cat retinal ganglion cells. J. Neurophysiol. 28: 833–849, 1965.
 165. Sahar, G., and J. M. Hollerbach. Planning of minimum‐time trajectories for robot arms. Proc. IEEE Int. Conf. Robotics and Automation, St. Louis, MO, March 1985, p. 751–758.
 166. Sakitt, B. A spring model and equivalent neural network for arm posture control. Biol. Cybern. 37: 227–234, 1980.
 167. Salisbury, J. K., and J. J. Craig. Articulated hands: force control and kinematic issues. Int. J. Robotics Res. 1 (1): 4–17, 1982.
 168. Saltzman, E. Levels of sensorimotor representation. J. Math. Psychol. 20: 91–163, 1979.
 169. Sanes, J. N. Inaccurate limb positioning after unexpected perturbations during movement. Soc. Neurosci. Abstr. 9: 630, 1983.
 170. Schiller, P. H. Central connections of the retinal ON and OFF pathways. Nature Lond. 297: 580–583, 1982.
 171. Schiller, P. H., B. L. Finlay, and S. F. Volman. Quantitative studies of single‐cell properties in monkey striate cortex. I. Spatiotemporal organization of receptive fields. J. Neurophysiol. 39: 1288–1319, 1976.
 172. Schiller, P. H., and J. G. Malpeli. Properties and tectal projections of monkey retinal ganglion cells. J. Neurophysiol. 40: 428–445, 1977.
 173. Shanmugam, K. S., F. M. Dickey, and J. A. Green. An optimal frequency domain filter for edge detection in digital pictures. IEEE Trans. Patt. Anal. Mach. Intell. PAMI‐1: 37–49, 1979.
 174. Shapley, R. M., and J. D. Victor. How the contrast gain control modifies the frequency responses of cat retinal ganglion cells. J. Physiol. Lond. 318: 161–179, 1981.
 175. Sillito, A. M. The contribution of inhibitory mechanisms to the receptive field properties of neurons in the striate cortex of the cat. J. Physiol. Lond. 250: 305–329, 1975.
 176. Sillito, A. M. Inhibitory processes underlying the directional specificity of simple, complex and hypercomplex cells in the cat's visual cortex. J. Physiol. Lond. 271: 699–720, 1977.
 177. Sillito, A. M., J. A. Kemp, J. A. Milson, and N. Berardi. A reevaluation of the mechanism underlying simple cell orientation selectivity. Brain Res. 194: 517–520, 1980.
 178. Slotine, J.‐J. E. The robust control of robot manipulators. Int. J. Robotics Res. 4 (2): 49–64, 1985.
 179. Snyder, W. E. Industrial Robots: Computer Interfacing and Control. Englewood Cliffs, NJ: Prentice‐Hall, 1985.
 180. Soechting, J. F. Effect of target size on spatial and temporal characteristics of a pointing movement in man. Exp. Brain Res. 54: 121–132, 1984.
 181. Soechting, J. F., and F. Lacquaniti. Invariant characteristics of a pointing movement in man. J. Neurosci. 1: 710–720, 1981.
 182. Spitzberg, R., and W. A. Richards. Broad band spatial filters in the human visual system. Vision Res. 15: 837–841, 1975.
 183. Spitzer, H., and S. Hochstein. Simple‐ and complex‐cell response dependencies on stimulation parameters. J. Neurophysiol. 53: 1244–1265, 1985.
 184. Stein, R. B. What muscle variable(s) does the nervous system control in limb movements? Behav. Brain Sci. 5: 535–577, 1982.
 185. Takase, K. Task‐oriented variable control of manipulator and its software serving system. Proc. IFAC Symp. Information—Control Problems in Manufacturing Tech., Tokyo, October 1977, p. 139–143.
 186. Thompson, W. B., and S. T. Barnard. Lower‐level estimation and interpretation of visual motion. IEEE Comput., August 1981, p. 20–28.
 187. Tolhurst, D. J. Separate channels for the analysis of the shape and the movement of a moving visual stimulus. J. Physiol. Lond. 231: 385–402, 1973.
 188. Tolhurst, D. J. Sustained and transient channels in human vision. Vision Res. 15: 1151–1155, 1975.
 189. Torre, V., and T. Poggio. On edge detection. IEEE Trans. Patt. Anal. March. Intell. PAMI‐8: 147–163, 1986.
 190. Tsai, R. Y., and T. S. Huang. Uniqueness and Estimation of Three‐Dimensional Motion Parameters of Rigid Objects With Curved Surfaces. Urbana: Univ. of Illinois Coordinated Sci. Lab., 1981. (Rep. R‐921.)
 191. Ullman, S. The Interpretation of Visual Motion. Cambridge, MA: MIT Press, 1979.
 192. Ullman, S. Recent computational studies in the interpretation of structure and motion. In: Human and Machine Vision, edited by J. Beck, B. Hope, and A. Rosenfeld. New York: Academic, 1983, p. 459–480.
 193. Ullman, S. Maximizing rigidity: the incremental recovery of 3‐D structure from rigid and rubbery motion. Perception 13: 255–274, 1984.
 194. Van Santen, J. P. H., and G. Sperling. Temporal covariance model of human motion perception. J. Opt. Soc. Am. 1: 451–473, 1984.
 195. Victor, J. D., and R. M. Shapley. Receptive field mechanisms of cat X and Y ganglion cells. J. Gen. Physiol. 74: 275–298, 1979.
 196. Wallach, H. On Perception. New York: Quadrangle, 1976.
 197. Wallach, H., and D. N. O'Connell. The kinetic depth effect. J. Exp. Psychol. 45: 205–217, 1953.
 198. Wallach, H., A. Weisz, and P. A. Adams. Circles and derived figures in rotation. Am. J. Psychol. 69: 48–59, 1956.
 199. Watson, A. B., and J. Nachmias. Patterns of temporal interaction in the detection of gratings. Vision Res. 17: 893–902, 1977.
 200. Watt, R. J., and M. J. Morgan. Mechanisms responsible for the assessment of visual location: theory and evidence. Vision Res. 23: 97–109, 1983.
 201. Watt, R. J., and M. J. Morgan. The recognition and representation of edge blur: evidence for spatial primitives in human vision. Vision Res. 23: 1465–1477, 1983.
 202. White, B. W., and G. E. Mueser. Accuracy in reconstructing the arrangement of elements generating kinetic depth displays. J. Exp. Psychol. 60: 1–11, 1960.
 203. Whiting, H. T. A. (editor). Human Motor Actions: Bernstein Reassessed. Amsterdam: North‐Holland, 1984.
 204. Wilson, H. R., and J. R. Bergen. A four mechanism model for threshold spatial vision. Vision Res. 19: 19–32, 1979.
 205. Wood, J. E., S. G. Meek, and S. C. Jacobsen. Quantitation of Human Shoulder Anatomy for Prosthesis Control. Salt Lake City: University of Utah Ctr. for Biomed. Design, October 1984.
 206. Yuille, A. L., and T. Poggio. Scaling theorems for zerocrossings. IEEE Trans. Patt. Anal. Mach. Intell. PAMI‐8: 15–25, 1986.
 207. Yuille, A. L., and T. Poggio. Fingerprint theorems for zero crossings. J. Opt. Soc. Am. 2: 683–692, 1985.

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Article Title: Artificial Intelligence: Computational Approach to Vision and Motor Control
 

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Ellen C. Hildreth, John M. Hollerbach. Artificial Intelligence: Computational Approach to Vision and Motor Control. Compr Physiol 2011, Supplement 5: Handbook of Physiology, The Nervous System, Higher Functions of the Brain: 605-642. First published in print 1987. doi: 10.1002/cphy.cp010515