Adaptive and predictive control of a simulated robot arm

International Journal of Neural Systems

Volumn 23, Issue 3, 2013, Pages

  Tolu, Silvia ^a   Vanegas, Mauricio ^b   Garrido, Jesús A ^c   Luque, Niceto R ^a   Ros, Eduardo ^a  

^a UNIVERSITY OF GRANADA   (Spain)

^b UNIVERSITY OF GENOA   (Italy)

^c Consorzio Interuniversitario per le Scienze Fisiche della Materia ^*  (Italy)

Author keywords

adaptive learning; Light weight robot; locally weighted projection regression; recurrent control architecture

Indexed keywords

ADAPTIVE CONTROL ARCHITECTURE; ADAPTIVE LEARNING; CONTROL ARCHITECTURE; INCREMENTAL LEARNING; LIGHT WEIGHT ROBOTS; LOCALLY WEIGHTED PROJECTION REGRESSIONS; NUMBER OF DEGREES OF FREEDOM; PREDICTIVE CONTROL;

LEARNING ALGORITHMS; LEARNING SYSTEMS; ROBOTIC ARMS;

COMPUTER SIMULATION;

ADAPTATION; ALGORITHM; ARM; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CEREBELLUM; COMPUTER SIMULATION; CYTOLOGY; FEEDBACK SYSTEM; HUMAN; MOVEMENT (PHYSIOLOGY); NERVE CELL; PHYSIOLOGY; PREDICTIVE VALUE; ROBOTICS; ARTICLE;

ADAPTATION, PHYSIOLOGICAL; ALGORITHMS; ARM; ARTIFICIAL INTELLIGENCE; CEREBELLUM; COMPUTER SIMULATION; FEEDBACK; HUMANS; MODELS, NEUROLOGICAL; MOVEMENT; NEURONS; PREDICTIVE VALUE OF TESTS; ROBOTICS;

EID: 84877295696     PISSN: 01290657     EISSN: None     Source Type: Journal    
DOI: 10.1142/S012906571350010X     Document Type: Article

References (46)

1. D. Marr, A theory of cerebellar cortex, J. Physiol. 202 (1969) 437-470.
2. J. S. Albus, A theory of cerebellar function, Math. Biosciences 10(1-2) (1971) 25-61.
3. M. Ito, Historical review of the significance of the cerebellum and the role of Purkinje cells in motor learning, Ann. New York Acad. Sci. 978 (2002) 273-288. (Pubitemid 36127117)
4. S. Ghosh-Dastidar and H. Adeli, Spiking neural networks, Int. J. Neural Syst. 19(4) (2009) 295-308.
5. M. Kawato, Feedback-error-learning neural network for supervised motor learning, in Advanced Neural Computers, ed. R. Eckmiller (Elsevier, North-Holland, 1990), pp. 365-372.
6. H. Gomi and M. Kawato, Adaptive feedback control models of the vestibulocerebellum and spinocerebellum, Biol. Cybern. 68(2) (1992) 105-114.
7. D. M. Wolpert, R. C. Miall and M. Kawato, Internal models in the cerebellum, Trends Cogn. Sci. 2(9) (1998) 338-347.
8. N. R. Luque, J. A. Garrido, R. R. Carrillo, S. Tolu and E. Ros, Adaptive cerebellar spiking model embedded in the control loop: Context switching and robustness against noise, Int. J. Neural Syst. 21(5) (2011) 385-401.
9. M. I. Jordan and D. E. Rumelhart, Forward models: Supervised learning with a distal teacher, Cogn. Sci. 16 (1992) 307-354.
10. J. Porrill and P. Dean, Recurrent cerebellar loops simplify adaptive control of redundant and nonlinear motor systems, Neural Comput. 19(1) (2007) 170-193.
11. M. Fujita, Adaptive filter model of the cerebellum, Biol. Cybern. 45 (1982) 195-206. (Pubitemid 13213497)
12. P. Dean, J. Porrill, C. Ekerot and H. Jorntell, The cerebellar microcircuit as an adaptive filter: Experimental and computational evidence, Nat. Rev. Neurosci. 11(1) (2010) 30-43.
13. M. Kawato Internal models for motor control and trajectory planning, Curr. Opin. Neurobiol. 9(6) (1999) 718-723.
14. V. Mohan and P. Morasso, Towards reasoning and coordinating action in the mental space, Int. J. Neural Syst. 4(17) (2007) 329-341. (Pubitemid 47258353)
15. M. Ito, Control of mental activities by internal models in the cerebellum, Nature Rev. Neurosci. 9(4) (2008) 304-313. (Pubitemid 351430830)
16. S. Schaal and N. Schweighofer, Computational motor control in humans and robots, Curr. Opin. Neurobiol. 15(6) (2005) 675-82. (Pubitemid 41682308)
17. D. M.Wolpert, Z. Ghahramani and M. I. Jordan, An internal model for sensorimotor integration, Science 269 (1995) 1880-1882.
18. R. C. Miall and D. M. Wolpert, Forward models for physiological motor control, Neural Netw. 9 (1996) 1265-1279. (Pubitemid 26413051)
19. T. J. Sejnowski, Storing covariance with nonlinearly interacting neurons, J. Math. Biol. 4(4) (1977) 303-321. (Pubitemid 8188217)
20. S. Vijayakumar and S. Schaal, Locally weighted projection regression: Incremental real time learning in high dimensional space, in Proc. 17th Int. Conf. on Machine Learning (ICML'00) (San Francisco, 2000), pp. 1079-1086.
21. S. Vijayakumar, A. D'Souza and S. Schaal, Incremental online learning in high dimensions, Neural Comput. 17 (2005) 2602-2634. (Pubitemid 41505186)
22. C. G. Atkeson, J. G. Hale, F. Pollick, M. Riley, S. Kotosaka, S. Schaul, T. Shibata, G. Tevatia, A. Ude, S. Vijayakumar, E. Kawato and M. Kawato, Using humanoid robots to study human behavior, Intell. Syst. Appl. IEEE 15(4) (2000) 46-56.
23. S. Schaal, C. G. Atkeson and S. Vijayakumar, Scalable techniques from nonparametric statistics for real time robot learning, Appl. Intell. 17(1) (2002) 49-60. (Pubitemid 34789897)
24. D. Nguyen-Tuong and J. Peters, Learning robot dynamics for computed torque control using local Gaussian processes regression, in Proc. ECSIS Symp. Learning and Adaptive Behaviour in Robotic Systems (LAB-RS'08) (2008
25. C. K. I. Williams and C. E. Rasmussen, Gaussian processes for regression, in Advances in Neural Information Processing Systems, Vol. 8 (MIT press, 1996), pp. 514-520.
26. A. J. Smola and B. Scholkopf, A tutorial on support vector regression, Stat. Comput. 14(3) (2004) 199-222. (Pubitemid 39063488)
27. G. Lebrun, C. Charrier, O. Lezoray and H. Cardot, Tabu search model selection for SVM, Int. J. Neural Syst. 18(1) (2008) 19-31. (Pubitemid 351700628)
28. O. Sigaud, C. Salaun and V. Padois, On-line regression algorithms for learning mechanical models of robots: A survey, Robot. Auton. Syst. 59(12) (2011) 1115-1129.
29. D. Mitrovic, S. Klanke and S. Vijayakumar, Adaptive optimal control for redundantly actuated arms, in Proc. 10th Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats (SAB'08) (2008) 93-102.
30. G. Hirzinger, J. Butterfa, M. Fischer, M. Grebenstein, M. Hahnle, H. Liu, I. Schafer and N. Sporer, A mechatronics approach to the design of light-weight arms and multifingered hands, in Proc. ICRA (2000), pp. 46-54.
31. A. Albu-Schaffer, S. Haddadin, C. Ott, A. Stemmer, T. Wimbock and G. Hirzinger, The DLR lightweight robot: Design and control concepts for robots in human environments, Ind. Robot 34(5) (2007) 376-385. (Pubitemid 47283169)
32. P. Van der Smagt, Cerebellar control of robot arms, Connect. Sci. 10 (January 1998) 301-320.
33. R. H. Middleton and G. C. Goodwin, Adaptive computed torque control for rigid link manipulators, Syst. Control Lett. 10(1) (1988) 9-16. (Pubitemid 18551781)
34. S. Arimoto and F. Miyazaki, Stability and robustness of PID feedback control for robot manipulators of sensory capability, in Robotics Research: First Int. Symp., eds. M. Brady and R. P. Paul (1984
35. P. van der Smagt, Benchmarking cerebellar control, Robot. Auton. Syst., 32 (2000) 237-51.
36. J. Nakanishi and S. Schaal, Feedback error learning and nonlinear adaptive control, Neural Netw. 17(10) (2004) 1453-1465. (Pubitemid 39487141)
37. D. M. Wolpert, Computational approaches to motor control, Trends Cogn. Sci. 1(6) (1997) 209-216. (Pubitemid 127425909)
38. P. R. Davidson and D. M. Wolpert, Widespread access to predictive models in the motor system: A short review, J Neural Eng. 2(3) (2005) 313-319.
39. P. Corke, A robotics toolbox for matlab, IEEE Robot. Autom. Mag. 3(1) (1996) 24-32. (Pubitemid 126553108)
40. S. Haddadin, A. Albu-Schaffer and G. Hirzinger, Safe physical Human-Robot interaction: Measurements, analysis and new insights, in Springer Tracts in Advanced Robotics (ISRR), eds. M. Kaneko and Y. Nakamura, Vol. 66 (Springer, 2007), pp. 395-407.
41. P. Wulff, M. Schonewille, M. Renzi, L. Viltono, M. Sasso-Pognetto, A. Badura, Z. Gao, F. E. Hoebeek, S. van Dorp, W. Wisden, M. Farrant and C. I. De Zeeuw, Synaptic inhibition of Purkinje cells mediates consolidation of vestibulo-cerebellar motor learning, Nature Neurosci. 12(8) (2009) 1042-1049.
42. T. Honda, T. Yamazaki, S. Tanaka and T. Nishino, A possible mechanism for controlling timing representation in the cerebellar cortex, in Int. Symp. Neural Networks (2010), pp. 67-76.
43. N. Schweighofer, K. Doya and F. Lay, Unsupervised learning of granule cell sparse codes enhances cerebellar adaptive control, Neurosci. 103 (2001) 35-50. (Pubitemid 32304345)
44. J. J. Craig, Introduction to Robotics: Mechanics and Control, 3rd edn. (Pearson/Prentice Hall, Upper Saddle River, New Jersey, 2005
45. W. Khalil and E. Dombre, Modeling Identification and Control of Robots (Hermes Penton Science, 2002
46. B. Bona and A. Curatella, Identification of industrial Robot Parameters for Advanced Model-Based Controllers Design, in: ICRA (2005), pp. 1693-1698.