Cerebellar Control of Robot Arms

Connection Science

Volumn 10, Issue 3-4, 1998, Pages 301-320

  Van Der Smagt, Patrick ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Cerebellum; Computational cerebellar models; Neural networks; Robot arm control; Robot dynamics

Indexed keywords

EID: 0032273342     PISSN: 09540091     EISSN: None     Source Type: Journal    
DOI: 10.1080/095400998116468     Document Type: Article

References (74)

1. Aguilar, J.M. & Contreras-Vidal, J.L. (1994) NAVITE: a neural network system for sensory-based robot navigation. In Proceedings of the World Congress on Neural Networks, Vol. II, pp. 177-182. Hillsdale, NJ: Lawrence Erlbaum.
2. Aiba, A., Kano, M., Chen, C., Stanton, M. Fox, G., Herrup, K., Zwingman, T. & Tonegawa, S. (1994) Deficient ccrcbcllar long-term depression and impaired motor learning in mGluR1 mutant mice. Cell, 79, 377-388.
3. Albus, J. (1971) A theory of cercbellar function. Mathematical Bioscience, 10, 25-61.
4. Albus, J. (1975) A new approach to manipulator control: the cerebellar model articulation controller (CMAC). Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, Series G, 97, 270-277.
5. Artola, A. & Singer, W. (1993) Long-term depression of excitatory synaptic transmission and its relationship to long-term potentiation. Trends in Neuroscience, 16, 480-487.
6. Atkeson, C. & Hollerbach, J. (1985) Kinematic features of unrestrained vertical arm movements. Journal of Neuroscience, 5, 2318-2330.
7. Barto, A.G., Buckingham, J.T. & Houk, J.C. (1995) A predictive switching model of cerebellar movement control. In D.S. Touretzky, M.C. Mozer & M.E. Hasselmo (Eds), Advances in Neural Information Processing Systems 8, pp. 138-144. Boston, MA: MIT Press.
8. Bell, C. (1811) Idea of a New Anatomy of the Brain; Submitted for the Observations of His Friends. London: Strahan and Preston.
9. Berthier, N.B., Singh, S. P., Barto, A G. & Houk, J.C. (1993) Distributed representation of limb motor programs in arrays of adjustable pattern generators. Journal of Cognitive Neuroscience, 5, 56-78.
10. Bliss, T.V.P. & Collingridge, G. (1993) A synaptic model of memory: long-term potentiation in the hippocampus. Nature, 361, 31-39.
11. Braake, H.A.B. te, van Can, E.J.L. & Verbruggen, H.B. (1998) Training of neural networks by a variable projection method. Neural Networks (in press).
12. Braitenberg, V. (1961) Functional interpretation of cerebellar histology. Nature, 190, 539-540.
13. Braitenberg, V. & Atwood, R.P. (1958) Morphological observations on the cerebellar cortex. Journal of Comparative Neurology, 109, 1-34.
14. Bullock, D. (1997) Cerebellar learning for context sensitive and critically timed coordination of multiple action channels. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 11-12. NIPS '97 Workshop.
15. Bullock, D. & Grossberg, S. (1988a) Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation. Psychological Review, 95, 49-90.
16. Bullock, D. & Grossberg, S. (1988b) Neuromuscular realization of planned trajectories. Neural Networks, 1, 329.
17. Bullock, D. & Grossberg, S. (1989) VITE and FLETE: neural modules for trajectory formation and tension control. In W. Hershberger (Ed.), Volitional Action, pp. 253-297. Amsterdam: North-Holland.
18. Bullock, D., Grossberg, S. & Mannes, C. (1993) A neural network model for cursive script production. Biological Cybernetics, 70, 15-28.
19. Buonomano, D.V. & Mauk, M.D. (1994) Neural network model of the cerebellum: temporal discrimination and the timing of motor responses. Neural Computation, 6, 38-55.
20. Chou, C.-P. & Hannaford, B. (1996) Measurement and modeling of McKibben pneumatic artificial muscles. IEEE Transactions on Robotics and Automation, 12, 90-102.
21. Contreras-Vidal, J., Grossberg, S. & Bullock, D. (1997) A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics. Learning and Memory, 3, 475-502.
22. Contreras-Vidal, J. & Lopez-Coronado, J. (1997) Adaptive cerebellar control of opponent muscles. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 13-16. NIPS '97 Workshop.
23. De Schutter, E. (1996) One cannot build theories of cerebellar function on shaky foundations: induction properties of long-term depression have to be taken into account. Behavioral and Brain Sciences, 19, 440-441.
24. De Schutter, E. (1997) A new functional role for cerebellar long term depression. Progress in Brain Research, 114, 529-542.
25. DeMers, D. & Kreutz-Delgado, K. (1997) Inverse kinematics of dextrous manipulators. In O. Omidvar & P. van der Smagt (Eds), Neural Systems for Robotics, pp. 75-116. Boston, MA: Academic Press.
26. Eccles, J.C., Ito, M. & Szentagothai, J. (1967) The Cerebellum as a Neuronal Machine. Berlin: Springer.
27. Fagg, A., Zelevinsky, L., Barto, A. & Houk, J. (1997a) Using crude corrective movements to learn accurate motor programs for reaching. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 20-24. NIPS '97 Workshop, Breckenridge, CO.
28. Fagg, A.H., Sitkoff, N., Houk, J.C. & Barto, A.G. (1997b) Cerebellar learning for control of a two-link arm in muscle space. In Proceedings of the IEEE Conference on Robotics and Automation, pp. 2638-2644.
29. Flourens, P. (1824) Recherches expérimentales sur les propriétés et les fonctions du système nerveux, dans les animaux vertébres. Paris: Cervot.
30. Franklin, B. (1774) Experiments and Observations on Electricity. London.
31. Gaudiano, P. & Grossberg, S. (1991) Vector associative maps: unsupervised real-time error-based learning and control of movement trajectories. Neural Networks, 4, 147-183.
32. Gaudiano, P. Guenther, F.H. & Zalama, E. (1997) The neural dynamics approach to sensory-motor control. In O. Omidvar & P. van der Smagt (Eds), Neural Systems for Robotics, pp. 153-194. Boston, MA: Academic Press.
33. Glickstein, M. (1994) Cerebellar agenesis. Brain, 117, 1209-1212.
34. Hirzinger, G. (1996) Towards a new robot generation for space, terrestrial, and medical applications. In Proceedings of the 2nd ECPD International Conference on Advanced Robotics, Intelligent Automation, and Active Systems, Vienna, Austria.
35. Hogan, N. (1984) An organizing principle for a class of voluntary movements. Journal of Neuroscience, 4, 2745-2754.
36. Hore, J. & Flament, D. (1988) Changes in motor cortex neural discharge associated with the development of cerebellar limb ataxia. Journal of Neurophysiology, 60, 1285-1302.
37. Houk, J.C. (1989) Cooperative control of limb movements by the motor cortex, brain-stem, and cerebellum. In R.M.J. Cotterill (Ed.), Models of Brain Function, pp. 309-325. Cambridge: Cambridge University Press.
38. Houk, J.C., Buckingham, J.T. & Barto, A.G. (1996) Models of the cerebellum and motor learning. Behavioral and Brain Sciences, 19, 368-383.
39. Houk, J.C., Singh, S.P., Fisher, C. & Barto, A.G. (1990) An adaptive sensorimotor network simulation inspired by the anatomy and physiology of the cerebellum. In W. Miller, R. Sutton & P. Werbos (Eds), Neural Networks for Control, pp. 301-348. Boston, MA: MIT Press.
40. Ito, M. (1984) The Cerebellum and Neural Control. New York: Raven Press.
41. Ito, M. Sakurai, M. & Tongroach, P. (1982) Climbing fiber induced depression of both mossy fiber responsiveness and glutamate sensitivity of cerebellar Purkinje cells. Journal of Physiology, 324, 113-134.
42. Jabri, M., Coenen, O., Huang, J. & Sejnowski, T. (1997) Sensorimotor integration and control. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 33-38. NIPS '97 Workshop.
43. Kano, M. (1996) Long-lasting potcntiation of GABAergic inhibitory synaptic transmission in cerebellar Purkinje cells: its properties and possible mechanisms. Behavioral and Brain Sciences, 19, 354-361.
44. Katayama, M. & Kawato, M. (1991) Learning trajectory and force control of an artificial muscle arm by parallel-hierarchical neural network model. In R. Lippmann, J. Moody & D. Touretzky (Eds), Advances in Neural Information Processing Systems, Vol. 3, pp. 436-442. San Mateo: Morgan Kaufmann.
45. Kawato, M. (1997) Multiple internal models in the cerebellum. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 17-19. NIPS '97 Workshop.
46. Kawato, M. & Gomi, H. (1992) The cerebellum and VOR/OKR learning models. Trends in Neurosciences, 15, 445-453.
47. Kraft, G. (1997) Optimized weight smoothing for CMAC neural networks. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 8-10. NIPS '97 Workshop.
48. Lang, M. (1997) A Real-time Implementation of a Neural-network Controller for Industrial Robotics. Doctoral dissertation, Institute of Aerospace Studies, University of Toronto.
49. Li, L. & Ögmen, H. (1994) Visually guided motor control: adaptive sensorimotor mapping with online visual error correction. In Proceedings of the World Congress on Neural Networks, Vol. 2, pp. 127-134. Hillsdale, NJ: Lawrence Erlbaum.
50. Lisberger, S.G. (1994) Neural basis for motor learning in the vestibuloocular reflex of primates. III. Computational and behavioral analysis of the sites of learning. Journal of Neurophysiology, 72, 974-998.
51. Lisberger, S.G., Pavelko, T.A., Bronte-Stewart, H.M. & Stone, L.S. (1994a) Neural basis for motor learning in the vestibuloocular reflex of primates. II. Changes in the responses of horizontal gaze velocity Purkinje cells in the cerebellar flocculus and ventral paraflocculus. Journal of Neurophysiology, 72, 954-973.
52. Lisberger, S.G., Pavelko, T.A. & Broussard, D.M. (1994b) Neural basis for motor learning in the vestibuloocular reflex of primates. I. Changes in the responses of brain stem neurons. Journal of Neurophysiology, 72, 928-953.
53. Marr, D. (1969) A theory of cerebellar cortex. Journal of Physiology, 202, 437-470.
54. Miall, R.C., Weir, D.J., Wolpert, D.M. & Stein, J.F. (1993). Is the cerebellum a Smith predictor? Journal of Motor Behavior, 25, 203-216.
55. Michie, D. & Chambers, R.A. (1968) BOXES: an experiment in adaptive control. In E. Dale & D. Michie (Eds), Machine Intelligence, Vol. 2, pp. 137-152. Edinburgh: Oliver and Boyd.
56. Miller, W.T., Glanz, F.H. & Kraft, L.G. (1990) CMAC: an associative neural network alternative to backpropagation. Proceedings of the IEEE, 78, 1561-1567.
57. Miller III, W.T. (1989) Real-time application of neural networks for sensor-based control of robots with vision. IEEE Transactions on Systems, Man, and Cybernetics, 19, 825-831.
58. Miller III, W.T., & Kun, A.L. (1997) Neural systems for robotics. In O. Omidvar & P. van der Smagt (Eds), Dynamic Balance of a Biped Walking Robot. Boston, MA: Academic Press.
59. Nelson, M. (1997) Adaptive motor control without a cerebellum. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 25-27. NIPS '97 Workshop.
60. Omidvar, O. & van der Smagt, P. (Eds) (1997) Neural Systems for Robotics. Boston, MA: Academic Press.
61. Paulin, M.G. (1989) A Kalman filter theory of the cerebellum. In M.A. Arbib & S. Amari (Eds), Dynamic Interactions in Neural Networks, pp. 241-259. Heidelberg, Berlin: Springer.
62. Paulin, M.G. (1993) The role of the cerebellum in motor control and perception. Brain, Behavior, and Evolution, 31, 39-50.
63. Schweighofer, N. (1995) Computational Models of the Cerebellum in the Adaptive Control of Movements. Doctoral dissertation, University of Southern California.
64. Simpson, J.I., Wylie, D.R. & De Zeeuw, C.I. (1996) On climbing fiber signals and their consequence(s). Behavioral and Brain Sciences, 19, 384-398.
65. Spoelstra, J., Arbib, M. & Schweighofer, N. (1997) Cerebellar control of a simulated biomimetic manipulator for fast movements. In P. van der Smagt & D. Bullock (Eds), Can Artificial Cerebellar Models Compete to Control Robots? Technical Report 515-97-28, Oberpfaffenhofen, pp. 28-32. NIPS '97 Workshop.
66. Thach, W., Goodkin, H. & Keating, J.G. (1992) The cerebellum and the adaptive coordination of movement. Annual Review of Neuroscience, 15, 403-442.
67. van der Smagt, P. (1994) Minimisation methods for training feed-forward networks. Neural Networks, 7, 1-11.
68. van der Smagt, P. (1995) Visual Robot Arm Guidance using Neural Networks. Doctoral dissertation, Department of Computer Systems, University of Amsterdam.
69. van der Smagt, P. & Bullock, D. (Eds) (1997) Can Artificial Cerebellar Models Compete, to Control robots? Technical Report 515-97-28, Oberpfaffenhofen. NIPS '97 Workshop (Downloadable from http: //www.robotic.dlr.de/Smagt/papers/).
70. van der Smagt, P., Groen, F. & van het Groenewoud, F. (1994) The locally linear nested network for robot manipulation. In Proceedings of the IEEE International Conference on Neural Networks, Orlando, FL, pp. 2787-2792.
71. van der Smagt, P., Groen, F. & Schulten, K. (1996) Analysis and control of a rubbertuator arm. Biological Cybernetics, 75, 433-440.
72. van der Smagt, P. & Hirzinger, G. (1998) Solving the ill-conditioning in neural network learning. In J. Orr, K. Müller & R. Caruana (Eds), Tricks of the Trade: How to Make Neural Networks Really Work. Lecture Notes in Computer Science. Berlin: Springer.
73. von Hofsten, C. (1979) Development of visually directed reaching: the approach phase. Journal of Human Movement Studies, 5, 160-168.
74. Wang, C. & Principe, J.C. (1997) On-line stochastic functional smoothing optimization for neural network training. IEEE Transactions on Systems, Man, and Cybernetics (submitted).