Optimal robot arm control using the minimum variance model

Journal of Robotic Systems

Volumn 22, Issue 11, 2005, Pages 677-690

  Simmons, Gavin ^a   Demiris, Yiannis ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DEGREES OF FREEDOM (MECHANICS); HUMAN COMPUTER INTERACTION; MATHEMATICAL MODELS; MATRIX ALGEBRA; OPTIMIZATION;

COMPUTATIONAL MODELS; MINIMUM VARIANCE MODEL; OPTIMAL CONTROL ALGORITHM;

ROBOTIC ARMS;

EID: 28044446547     PISSN: 07412223     EISSN: None     Source Type: Journal    
DOI: 10.1002/rob.20092     Document Type: Article

References (32)

1. G. Simmons and Y. Demiris, Biologically inspired optimal robot arm control with signal-dependent noise, Proc 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004.
2. Y. Uno, M. Kawato, and R. Suzuki, Formation and control of optimal trajectory in human multijoint arm movement: Minimum torque-change model, Biol. Cybern. 61 (1989), 89-101.
3. P. Morasso, Spatial control of arm movements, Exp. Brain Res. 42 (1981), 223-227.
4. R.A. Schmidt, Motor learning and performance: From principles to practice, Human Kinetics, Champaign, IL, 1991.
5. P.M. Fitts, The information capacity of the human motor system in controlling the amplitude of movement, J. Exp. Psychol. 47:(6) (1954), 381-391.
6. A.H. Fagg, L. Zelevinsky, A.G. Barto, and J.C. Houk, Using crude corrective movements to learn accurate motor programs for reaching, Extended Abstracts of NIPS '97 Workshop: Can Artificial Cerebellar Models Compete to Control Robots? 1997, Chap. 6, pp. 20-24.
7. A.H. Fagg, A.G. Barto, and J.C. Houk, Learning to reach via corrective movements, Proc Tenth Yale Workshop on Adaptive and Learning Systems, 1998, pp. 179-185.
8. J. Accot and S. Zhai, Beyond Fitts' law: Models for trajectory-based HCI tasks, Proc of CHI '97, 1997, pp. 295-302.
9. F. Lacquaniti, C.A. Terzuola, and P. Vivani, The law relating kinematic and figural aspects of drawing movements, Acta Psychol. 54 (1983), 115-130.
10. P. Viviani and R. Schneider, A developmental study of the relationship between geometry and kinematics in drawing movements, J. Exp. Psychol. Hum. Percept. Perform. 17 (1991), 198-218.
11. M.J.E. Richardson and T. Flash, Comparing smooth arm movements with the two-thirds power law and the related segmented-control hypothesis, J. Neurosci. 22:(18) (2002), 8201-8211.
12. G.E. Loeb, W. Levine, and J. He, Understanding sensorimotor feedback through optimal control, Symposium on Quantitative Biology 55 (1990), 791-803.
13. T. Flash and T.J. Sejnowski, Computational approaches to motor control, Curr. Opin. Neurobiol. 11 (2001), 655-662.
14. Z. Luo, M. Svinin, K. Ohta, T. Odashima, and S. Hosoe, On optimality of human arm movements, Proc IEEE International Conference on Robotics and Biomimetics 2004, p. 447.
15. E. Bizzi, F.A. Mussa-Ivaldi, and S. Giszter, Computations underlying the execution of movement: A biological perspective, Science 253 (1991), 287-291.
16. H. Gomi and M. Kawato, Equilibrium-point control hypothesis examined by measured arm stiffness during multijoint movement, Science 272 (1996), 117-120.
17. F.A. Mussa-Ivaldi, Motor primitives, force-fields and the equilibrium point theory, in From basic motor control to functional recovery, N. Gantchev and G. N. Gantchev (Editors), Academic Publishing House "Prof. M. Drinov", Sofia, 1999, pp. 392-398.
18. M.R. Hinder and T.E. Milner, The case for an internal dynamics model versus equilibrium point control in human movement, J Physiol 549:(3) (2003), 953-963.
19. E. Nakano, H. Imamizu, R. Osu, Y. Uno, H. Gomi, T. Yoshioka, and M. Kawato, Quantitative examinations of internal representations for arm trajectory planning: Minimum commanded torque change model, J. Neurophysiol. 81:(5) (1999), 2140-2155.
20. Y. Wada and M. Kawato, A via-point time optimization algorithm for complex sequential trajectory formation, Neural Networks 17 (2004), 353-364.
21. T. Flash and N. Hogan, The coordination of arm movements: An experimentally confirmed mathematical model, J. Neurosci. 5:(7) (1985), 1688-1703.
22. J.G. Hale and F.E. Pollick, Biomimetic synthesis for the upper limb based on human motor production, Proc 7th International Conference on Simulation of Adaptive Behaviour, 2002.
23. C.M. Harris and D.M. Wolpert, Signal-dependent noise determines motor planning, Nature 394 (1998), 780-784.
24. H. Miyamoto, E. Nakano, D.M. Wolpert, and M. Kawato, TOPS (Task Optimization in the Presence of Signal-dependent noise) model, Systems and Computers in Japan 35:(11) (2004), 48-58.
25. H. Miyamoto, J. Morimoto, K. Doya, and M. Kawato, Reinforcement learning with via-point representation, Neural Networks 17 (2004), 299-305.
26. S.J. Goodbody and D.M. Wolpert, Temporal and amplitude generalization in motor learning, J. Neurophysiol. 79 (1998), 1825-1838.
27. H. Miyamoto, D.M. Wolpert, and M. Kawato, Computing the optimal trajectory of arm movement: The TOPS (Task Optimization in the presence of signal-dependent noise) model, Biologically inspired robot behaviour engineering, vol. 109, chapter 14, Studies in fuzziness and soft computing, Springer, New York, 2003, pp. 395-416.
28. E. Todorov and M.I. Jordan, Optimal feedback control as a theory of motor coordination, Nat. Neurosci. 5:(11) (2002), 1226-1235.
29. M. Pomplun and M. Matarié, Evaluation metrics and results of human arm movement imitation, Proc 1st IEEE-RAS International Conference on Humanoid Robotics, 2000.
30. Y. Demiris and G. Hayes, Imitation as a dual-route process featuring predictive and learning components: A biologically-plausible computational model, Imitation in animals and artifacts, MIT Press, Cambridge, MA, 2002, Chap 13.
31. Y. Demiris and M. Johnson, Distributed, predictive perception of actions: A biologically inspired robotics architecture for imitation and learning, Connect Sci J 15:(4) (2003), 231-243.
32. G. Simmons and Y. Demiris, Imitation of human demonstration using a biologically inspired modular optimal control scheme, Proc 2004 IEEE-RAS/ RSJ International Conference on Humanoid Robots, 2004.