Representation of robot motion control skill

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 30, Issue 2, 2000, Pages 219-238

  Dordević, Goran S ^a   Rašić, Milan ^a   Kostic ^a  

^a UNIVERSITY OF NI   (Serbia)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; EXTRAPOLATION; INTELLIGENT ROBOTS; INTERPOLATION; KINEMATICS; MATHEMATICAL MODELS; MOTION COMPENSATION; REDUNDANCY; ROBOT LEARNING;

FUNCTION APPROXIMATORS; SKILLED ROBOTICS;

ANTHROPOMORPHIC ROBOTS;

EID: 0034187265     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/5326.868444     Document Type: Article

References (125)

1. J. S. Albus, "Mechanics of planning, and problem solving in the brain," Math. Biosci., vol. 45, pp. 247-293, Aug. 1979.
2. J. A. Adams, "Historical review, and appraisal of research on the learning, retention, and transfer of human motor skills," Psychol. Bull., vol. 101, pp. 41-74, 1987.
3. M. A. Arbib, "Programs, schemas, and neural networks for control of hand movements: Beyond the RS framework," in Attention and Performance XIII, M. Jeannerod, Ed. Hillsdale, NJ: Lawrence Erlbaum, 1990, pp. 111-138.
4. C. G. Atkeson, "Using locally weighted regression for robot learning," in IEEE Conf. Robot Automat., Sacramento, CA, Apr. 9-11, 1991, pp. 958-963.
5. C. H. An, C. G. Atkeson, and J. M. Hollerbach, Model-Based Control of a Robot Manipulator. Cambridge, MA: MIT Press, 1988.
6. J. E. Albus, A. J. Barbera, and R. N. Nagel, "Theory, and practice of hierarchical control," in P roc. 23rd IEEE Computer Society Int. Conf., 1.
7. S. Arimoto, S. Kawamura, and F. Miyasaki, "Bettering operation of robots by learning," J. Robot. Syst., vol. 1, no. 1, pp. 123-140, 1984.
8. C. Archibald and E. Petriu, "Computational paradigm for creating, and executing sensor based robot skills," in Proc. 24th Int. Symp. Industrial Robots, 1.
9. C. G. Atkeson and D. J. Reinkensmeyer, "Using associative content - Addressable memories to control robots," in IEEE Conf. Decision Control, Dec. 1988, pp. 792-799.
10. H. Asada and B.-H. Yang, "Skill acquisition from human experts through pattern processing of teaching data," in IEEE Int. Conf. Robot. Automat., 1.
11. N. Berstein, The Coordination, and Regulation of Movement. London, U.K.: Pergamon, 1967.
12. C. Baroglio, G. Attilio, M. Kaiser, M. Nuttin, and R. Piola, "Learning controllers for industrial robots," Mach. Learn., 1.
13. P. Bondi, G. Casalino, and L. Gambardella, "On the iterative learning control theory for robotic manipulators," IEEE J. Robot. Automat., vol. 4, pp. 14-22, Feb. 1988.
14. N. Bhushan and R. Shadmehr, "Evidence for a forward dynamics model in human adaptive motor control," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1999, vol. 11, pp. 3-9.
15. _, "Computational nature of human adaptive control during learning of reaching movements in force fields," Biol. Cybern., vol. 81, pp. 39-60, 1999.
16. T. Brashers-Krug, R. Shadmehr, and E. Todorov, "Catastrophic interference in human motor learning," in Advances in Neural Information Processing Systems, G. Tesauro, D. S. Touretzky, and T. K. Leen, Eds: MIT Press, 1995, vol. 7, pp. 19-26.
17. G. A. Bekey, H. Liu, R. Tomovic, and W. J. Karplus, "Knowledge - Based control of grasping in robot hands using heuristic from human motor skills," IEEE Trans. Robot. Automat., vol. 9, pp. 709-721, Dec. 1993.
18. A. Barto, R. Sutton, and C. Anderson, "Neuronlike adaptive elements that can solve difficult learning problems," IEEE Trans. Syst., Man, Cybern., vol. 13, pp. 834-846, 1983.
19. D. R. Baker and C. W. Wampler II, "On the inverse kinematics of redundant manipulators,", vol. 7, Mar./Apr. 1988.
20. J. J. Craig, Introduction to Robotics - Mechanics and Control. Reading, MA: Addison-Wesley, 1986.
21. T. F. Chan and R. V. Dubey, "A weighted least-norm solution based scheme for avoiding joint limits for redundant joint manipulators," IEEE Trans. Robot. Automat., vol. 11, pp. 286-292, Apr. 1995.
22. D. Cohn, Z. Ghahramani, and M. Jordan, "Active learning with statistical models," in Advances in Neural Information Processing Systems, G. Tesauro, D. Touretzky, and T. Leen, Eds. Cambridge, MA: MIT Press, 1995, vol. 7.
23. P. Chen and C. L. McAulay, "Robot kinematics learning computations using polynomial neural networks," in IEEE Conf. Robot. Automat., Cincinnati, OH, May 13-18, 1991, pp. 2638-2643.
24. G. Dordević, "A new approach to off-line skill acquisition for robot manipulator in complex trajectory following tasks," Ph.D. Dissertation, Univ. Nis, Nis, Yugoslavia, Oct. 1995.
25. G. Dordević and V. Potkonjak, "Skill acquiring in robotics by successive approximation of nominal control sequences," in P roc. IEEE Conf. Systems, Man, Cybernetics, Vancouver, BC, Canada, 1995, pp. 3700-3705.
26. _, "Successive approximations method for complex motor skill acquisition in robotics," in Proc. 2nd Int. Workshop MCPA, Pisa, Italy, Feb. 10-12, 1997, pp. 187-192.
27. G. Dordević, V. Potkonjak, M. Rašić, and D. Kostić, "Skill-based inverse kinematics of redundant robots," in Proc. 3rd ECPD Conf. ARIAAS, Sept. 15-18, 1997, pp. 301-306.
28. G. Dordević, M. Rašić, D. Kostić, and V. Potkonjak, "Extending universality of redundancy resolution skills," in Proc. 4th ECPD Conf. AARIAS, Moscow, Russia, Aug. 26-28, 1998, pp. 396-401.
29. _, "From inverse kinematic skills to inverse kinematic behavior of redundant robots," in Proc. 4th ECPD Conf. AARIAS, Moscow, Russia, Aug. 26-28, 1998, pp. 384-389.
30. G. Dordević, M. Rašić, D. Kostić, and D. Šurdilovic, "Learning of inverse kinematics behavior of redundant robot," in IEEE Conf. Robot. Automat., 1, pp. 3165-3170.
31. R. Dillmann, M. Kaiser, and A. Ude, "Acquisition of elementary robot skills from human demonstration," in Int. Symp. Intelligent Robot. Syst. (SIRS'95), Pisa, Italy, 1995.
32. D. DeMers and K. Kreutz-Delgado, "Issues in learning global properties of the robot kinematics mapping," in Proc. IEEE Int. Conf. Robotics Automation, Atlanta, May 1993, pp. 205-212.
33. _, "Solving inverse kinematics for redundant manipulators," in Neural Systems for Robotics, O. Omidvar and P. van der Smagt, Eds. New York: Academic, 1997, pp. 75-112.
34. N. Delson and H. West, "Robot programming by human demonstration: The use of human variation in identifying obstacle free trajectories," in IEEE Conf. Robot., Automat., San Diego, CA, 1994, pp. 564-571.
35. _, "Robot programming by human demonstration: Adaptation, and inconsistency in constrained motion,", Minneapolis, MM, Apr. 22-28, 1996, pp. 30-36.
36. M. Eldracher, "Neural subgoal generation with subgoal graph: An approach," in World Congr. Neural Networks, vol. 2, 1994, pp. 142-146.
37. M. Eldracher, D. Hernàndez, and M. Kinder, Neural modeling of the kinematics of robot trajectories, in Statusseminar des BMFT, Neuro-Informatik, Maurach, Germany, pp. 227-236, 1992.
38. M. Eldracher, A. Staller, and R. Pompl, "Function approximation with continuous-valued functions in CMAC," TU-Muenchen, Germany, Tech. Rep. FKI-199-94, Dec. 1994.
39. T. Flash and N. Hogan, "The coordination of arm movements: An experimentally confirmed mathematical model," J. Neurosci., vol. 5, pp. 1688-1703, July 1985.
40. O. Fuentes and R. C. Nelson, "Learning dexterous manipulation skills for multifingered robot hands," Comput. Sci. Dept., Univ. Rochester, Rochester, NY, TR 613, 1996.
41. _, "The virtual tool approach to dexterous telemanipulation," in IEEE Conf. Robot. Automat., Minneapolis, MM, 1996, pp. 1700-1705.
42. B. Fardanesh and J. Rastegar, "A new model - Based tracking controller for robot manipulators using trajectory pattern inverse dynamics," IEEE Trans. Robot. Automat., vol. 8, pp. 279-285, Apr. 1992.
43. J. R. Flanagan and A. M. Wing, "The role of internal models in motion planning, and control: Evidence from grip force adjustment during movements of hand-held loads," J. Neurosci., vol. 17, pp. 1519-1528, Feb. 1997.
44. V. Gullapalli, "Reinforcement learning, and its application to control," Ph.D. Dissertation, CMU, Feb. 1992.
45. V. Gullapalli, A. Barto, and R. Grupen, "Learning admittance mappings for force - Guided assembly," in IEEE Conf. Robot. Automat., San Diego, CA, 1994, pp. 2633-2638.
46. V. Gullapalli, J. A. Franklin, and H. Benbrahim, "Acquiring robot skills via reinforcement learning," IEEE Contr. Syst. Mag., pp. 13-24, Feb. 1994.
47. D. Gorinevsky, D. E. Torfs, and A. A. Goldenberg, "Learning approximation of feedforward control dependence on the task parameters with application to direct-drive manipulator tracking," IEEE Trans. Robot. Automat., vol. 13, pp. 567-580, Aug. 1997.
48. G. H. Golub and C. F. van Loan, Matrix Computations. London, U.K.: North Oxford, 1986.
49. F. B. Hildebrand, Introduction to Numerical Analysis, 2nd ed. New York: McGraw-Hill, 1974.
50. J. C. Houk, J. T. Buckingham, and A. G. Barto, "Models of the cerebellum, and motor learning," Behav. Brain Sci., vol. 19, no. 3, pp. 368-383, 1996.
51. G. Hirzinger, B. Brunner, J. Dietrich, and J. Heindl, "Sensor-based space robotics - ROTEX, and its telerobotic features," IEEE Trans. Robot. Automat., vol. 9, pp. 649-663, Oct. 1993.
52. G. Hovland, P. Sikka, and B. McCarragher, "Skill acquisition from human demonstration using a hidden Markov model," in IEEE Conf. Robot. Automat., Minneapolis, MM, 1996, pp. 2706-2711.
53. S. Hirai, T. Sato, T. Matsui, and M. Kakikura, "Integration of a task knowledge base, and a cooperative maneuvering system for the telerobot 'MEISTER'," in Proc. IEEE Int. Workshop Intell. Robot. Syst., 1, pp. 349-354.
54. T. Hasegawa, T. Suehiro, and K. Takase, "A model-based manipulation system with skill-based execution," IEEE Trans. Robot. Automat., vol. 8, no. 5, pp. 535-544, Oct. 1992.
55. M. Jeannerod, The Neural, and Behavioral Organization of Goal-Directed Movements. Oxford, U.K.: Clarendon , 1988.
56. M. I. Jordan, Computational Aspects of Motor Control, and Motor Learning, H. Heuer and S. W. Keele, Eds. New York: Academic, July 1996.
57. M. I. Jordan and D. E. Rumelhart, "Forward models: Supervised learning with a distal teacher," Cogn. Sci., vol. 16, pp. 307-354, 1992.
58. M. Kaiser, "Time-delay neural networks for control," in Proc. 4th Int. Symp. Robot Control (SYROCO'94), Capri, Italy, 1994.
59. M. Kawato, "Multiple internal models in the cerebellum," in Adv. Neural Inform. Process. Syst., Dec. 1997, vol. 9.
60. M. Kinder and T. Brychcy, "Path planning for six-joint manipulators by generalization from example paths," TU-Muenchen, Tech. Rep. FKI-192-94, May 1994.
61. M. Kaiser and R. Dillmann, "Hierarchical learning of efficient skill application for autonomous robots," in Int. Symp. Intelligent Robot. Syst. (SIRS'95), Pisa, Italy, 1995.
62. _, "Building elementary robot skills from human demonstration," in IEEE Conf. Robot. Automat., Minneapolis, MM, 1996, pp. 2700-2705.
63. S. Kawamura and N. Fukao, "Interpolations for input torque patterns obtained through learning control," in Proc. ICARCV'94, Singapore, Nov. 9-11, 1994, pp. 2194-2198.
64. _, "A time-scale interpolation for input torque patterns obtained through learning control on constrained robot motions," in IEEE Conf. Robot. Automat., 1, pp. 2156-2161.
65. C. A. Klein and C.-H. Huang, "Review of pseudoinverse control for use with kinematically redundant manipulators," IEEE Trans. Syst., Man, Cybern., vol. 13, pp. 245-250, Mar./Apr. 1983.
66. M. Kaiser, H. Friedrich, and R. Dillmann, "Obtaining good performance from a bad teacher," in Proc. Programming Demonstration Learning Examples Workshop ML'95, 1.
67. M. Kawato, K. Furukawa, and R. Suzuki, "A hierachical neural network model for control, and learning of voluntary movement," Biol. Cybern., vol. 57, pp. 169-185, 1987.
68. M. Kaiser, A. Giordana, and M. Nuttin, "Integrated acquisition, execution, evaluation, and tuning of elementary skills for intelligent robots," in IFAC Symp. AIRTC, 1.
69. Y. Kuniyoshi, M. Inaba, and H. Inoue, "Learning by watching: Extracting reusable task knowledge from visual observation of human performance," IEEE Trans. Robot. Automat., vol. 10, pp. 799-822, Dec. 1994.
70. S. Kieffer, V. Morellas, and M. Donath, "Neural network learning of the inverse kinematic relationships for a robot arm," in IEEE Proc. Robot. Automat., 1, pp. 2418-2425.
71. K. Kiguchi and D. S. Nesculescu, and T. Fukuda, "Neural networks application for motion prediction of robot manipulators," in Proc. 3rd Conf. ARIAAS, Sept. 15-17, 1997, pp. 215-219.
72. M. Kaiser, A. Retey, and R. Dillmann, "Robot skill acquisition via human demonstration," in Proc. Conf. Advanced Robotics (ICAR'95), 1.
73. S. W. Keele and J. J. Summers, "The structure of motor programs," in Motor Control - Issues and Trends, G. E. Stelmach, Ed. San Diego, CA: Academic, 1976, pp. 109-142.
74. B. Kröse and P. van der Smagt, An Introduction to Neural Networks, 8th ed. Amsterdam, The Netherlands, Nov. 1996.
75. K.-Y. Kim and Y.-S. Yoon, "Practical inverse kinematics of a kinematically redundant robot using a neural network," Adv. Robot., vol. 6, no. 4, pp. 431-440, 1992.
76. S. Liu and H. Asada, "Transfer of human skills to robots: Learning from human demonstration for building on adaptive control system," in Proc. American Control Conf., 1.
77. _, "Task-level robot adaptive control based on human teaching data, and its application to deburring," in Proc. Amer. Control Conf., 1.
78. S. Lee and J. Chen, "Skill learning from observations," in IEEE Conf. Robot. Automat., San Diego, CA, 1994, pp. 3245-3253.
79. _, "Robot skill discovery based on observed data," in IEEE Conf. Robot. Automat., Minneapolis, MM, 1996, pp. 2694-2699.
80. C. L. Lawson and R. J. Hanson, Solving Least Squares Problems. Englewood Cliffs, NJ: Prentice-Hall, 1974.
81. F. Lange and G. Hirzinger, "Learning to improve the path accuracy of position controlled robots," in IEEE/RSJ/GI Conf. Intelligent Robots Systems, München, Germany, Sept. 1994.
82. _, "Learning of a controller for nonrecurring fast movements," J. Adv. Robot., Apr. 1996.
83. Y. Li and N. Zeng, "A Neural network based inverse kinematics solution in robotics," in Neural Networks in Robotics, G. A. Bekey and K. Y. Goldberg, Eds. Norwell, MA: Kluwer, 1993.
84. P. Morasso, "Spatial control of movements," Exper. Brain Res., vol. 42, pp. 223-227, 1981.
85. W. T. Miller, III, "Sensor-based control of robotic manipulators using a general learning algorithm," IEEE J. Robot. Automat., vol. 3, pp. 157-165, Apr. 1987.
86. A. Moore, "Efficient memory-based learning for robot control," Ph.D. Dissertation, Univ. Cambridge, Cambridge, U.K., Nov. 1990.
87. Sensorimotor Learning, M. A. Arbib, Ed., MIT Press, Cambridge, MA, 1996.
88. J. D. Morrow, "Sensorimotor primitives for programming robotic assembly skills," Ph.D. Dissertation, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, May 1997.
89. W. T. Miller, F. H. Glanz, and L. G. Kraft, "Application of a learning algorithm to the control of robotic manipulators," Int. J. Robot. Res., vol. 6, no. 2, pp. 84-98, 1987.
90. J. D. Morrow and P. K. Khosla, "Sensorimotor primitives for robotic assembly skills," in IEEE Conf. Robot. Automat., 1, pp. 1894-1899.
91. _, "Manipulation task primitives for composing robot skills," in Proc. IEEE Conf. Robot. Automat., 1, pp. 3354-3359.
92. T. Martinetz, H. Ritter, and K. Schulten, "Learning of visuo motor-coordination of a robot arm with redundant degrees of freedom," in Parallel Processing in Neural Systems, and Computers, R. Eckmiller, G. Hartmann, and G. Hauske, Eds. Amsterdam, The Netherlands: Elsevier, 1990, pp. 431-434.
93. Y. Nakamura, Advanced Robotics: Redundancy, and Optimization. Reading, MA: Addison-Wesley, 1991.
94. M. C. Nechyba, "Learning, and Validation of Human Control Strategies," Ph.D. Disertation, Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, May 1998.
95. M. C. Nechyba and Y. Xu, "On the fidelity of human skill models," in IEEE Conf. Robot. Automat., Minneapolis, MM, 1996, pp. 2688-2693.
96. F. Pourboghrat, "Neural networks for learning inverse kinematics of redundant manipulators," in Proc. 32nd Midwest Symp. Circuits Systems, 1, pp. 760-762.
97. T. Poggio, "A theory of how the brain might work,", MIT A.I. Memo1253 and C.B.I.P. Memo 50, Dec. 1990.
98. P. K. Pook, "Teleassistance: Using Deictic Gestures to Control Robot Action," Ph.D. Dissertation, Comput. Sci. Dept., Univ. Rochester, Rochester, NY, Sept. 1995.
99. W. Press, B. Flannery, S. Teukolsky, and W. Vetterling, Numerical Recipes in C, 4th ed. Cambridge, U.K.: Cambridge Univ. Press, 1990.
100. T. Poggio and F. Girosi, "Networks for approximation, and learning," Proc. IEEE, vol. 78, pp. 1481-1496, Sept. 1990.
101. W. Risse and M. Hiller, "Dextrous motion control of a redundant SCARA robot," in IECON, Aachen, Germany, Sept. 1998, pp. 2446-2451.
102. H. Ritter, T. Martinetz, and K. J. Schulten, "Topology conserving maps for learning visuo-motor-coordination," Neural Networks, vol. 2, pp. 159-168, 1989.
103. A. R. Schmidt, Motor Control, and Learning - A Behavioral Emphasis. Champaign, IL: Human Kinetics, 1988.
104. T. H. Speeter, "Primitive based control of the Utah/MIT hand," in Proc. IEEE Conf. Robot. Automation, 1, pp. 866-877.
105. T. D. Sanger, "A practice strategy for robot learning control," in Adv. Neural Inform. Process. Syst., 1, vol. 5, pp. 335-341.
106. J. G. Schneider, "Robot skill learning through intelligent experimentation," Ph.D. Dissertation, Univ. Rochester, Rochester, NY, 1995.
107. T. D. Sanger, "Optimal movement primitives," in Proc. Advances Neural Information Processing Systems 7, 1, pp. 1023-1030.
108. T. Shibata, T. Abe, K. Tanie, and M. Nose, "Motion planning of a redundant manipulator based on criteria of skilled operators," in IEEE Conf. Systems, Man, Cybernetics '95, Vancouver, BC, Canada, Oct. 22-26, 1995, pp. 3730-3735.
109. M. Salganicof and K. R. Bajcsy, "Robotic sensorimotor learning in continuous domains," in IEEE Conf. Robot., and Automat., Nice, France, 1992, pp. 2045-2050.
110. R. Shadmehr, T. Brashers-Krug, and F. Mussa-Ivaldi, "Interference in learning internal models of inverse dynamics in humans," in Advances in Neural Information Processing Systems, G. Tesauro, D. S. Touretzky, and T. K. Leen, Eds. Cambridge, MA: MIT Press, 1995, vol. 7, pp. 1117-1124.
111. R. Shadmehr and F. A. Mussa-Ivaldi, "Adaptive representation of dynamics during learning of a motor task," J. Neurosci., vol. 14, pp. 3208-3224, 1994.
112. L. Sciavicco and B. Siciliano, Modeling and Control of Robot Manipulators. New York: McGraw-Hill, 1996.
113. K. Takayama and H. Kano, "A new approach to synthesizing free motions of robotic manipulators based on a concept of unit motions," IEEE Trans. Syst., Man, Cybern., vol. 25, no. 3, pp. 453-463, Mar. 1995.
114. S. G. Tzafestas and S. N. Raptis, "Neural networks for inverse robot kinematics," in Proc. 3rd Conf. ARIAAS, Sept. 15-17, 1997, pp. 398-403.
115. K. Tanaka, M. Shimizu, and K. Tsuchiya, "A solution to an inverse kinematics problem of a redundant manipulator using neural networks," in Artificial Neural Networks, K. Kohonen, Ed. Amsterdam, The Netherlands: Elsevier, 1991.
116. Y. Uno, M. Kawato, and R. Suzuki, "Formation, and control of optimal trajectory in human multijoint arm movements," Biol. Cybern., vol. 61, pp. 81-101, 1989.
117. C. W. Wampler II, "The inverse function approach to kinematic control of redundant manipulators," in Proc. ACC, Atlanta, GA, 1988.
118. C. J. C. H. Watkins and P. Dayan, "Q-learning," Mach. Learn., vol. 8, pp. 279-292, 1992.
119. D. M. Wolpert, "Computational approaches to motor control," Trends Cogn. Sci., vol. 1, no. 6, pp. 209-216, 1997.
120. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan, "An internal model for sensorimotor learning," Science, vol. 269, pp. 1880-1882, 1995.
121. D. M. Wolpert, R. C. Miall, and M. Kawato, "Internal models in the cerebellum," Trends Cogn. Sci., vol. 2, no. 9, pp. 338-347, 1998.
122. Y. Xu and M. Nechyba, "Fuzzy inverse kinematic mapping: Rule generation, efficiency, and implementation,", Tech. Rep. CMU-RI-TR-93-02, 1993.
123. Y. Xu and J. Yang, "Toward human-robot coordination: Skill modeling, and transferring via hidden Markov model," in IEEE Conf. Robot. Automat., 1, pp. 1906-1911.
124. K.-Y Young and S.-J. Shiah, "An approach to enlarge learning space coverage for robot learning control," IEEE Trans. Fuzzy Syst., vol. 5, pp. 511-522, Nov. 1997.
125. J. Yang, Y. Xu, and C. S. Chen, "Hidden Markov model approach to skill learning, and its application to telerobotics," IEEE Trans. Robot. Automat., vol. 10, pp. 621-631, Oct. 1994.