Adaptive optimal feedback control with learned internal dynamics models

Studies in Computational Intelligence

Volumn 264, Issue , 2010, Pages 65-84

  Mitrovic, Djordje ^a   Klanke, Stefan ^a   Vijayakumar, Sethu ^a  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 74049127949     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-05181-4_4     Document Type: Conference Paper

References (32)

1. Abbeel, P., Quigley, M., Ng, A.Y.: Using inaccurate models in reinforcement learning. In: Proc. Int. Conf. on Machine Learning (ICML), pp. 1-8 (2006)
2. Atkeson, C.G.: Randomly sampling actions in dynamic programming. In: Proc. Int. Symp. on Approximate Dynamic Programming and Reinforcement Learning, pp. 185-192 (2007)
3. Atkeson, C.G., Moore, A., Schaal, S.: Locally weighted learning for control. AI Review 11, 75-113 (1997)
4. Atkeson, C.G., Schaal, S.: Learning tasks from a single demonstration. In: Proc. Int. Conf. on Robotics and Automation (ICRA), Albuquerque, New Mexico, pp. 1706-1712 (1997)
5. Bertsekas, D.P.: Dynamic programming and optimal control. Athena Scientific, Belmont (1995)
6. Conradt, J., Tevatia, G., Vijayakumar, S., Schaal, S.: On-line learning for humanoid robot systems. In: Proc. Int. Conf. on Machine Learning (ICML), pp. 191-198 (2000)
7. Corke, P.I.: A robotics toolbox for MATLAB. IEEE Robotics and Automation Magazine 3(1), 24-32 (1996)
8. D'Souza, A., Vijayakumar, S., Schaal, S.: Learning inverse kinematics. In: Proc. Int. Conf. on Intelligence in Robotics and Autonomous Systems (IROS), Hawaii, pp. 298-303 (2001)
9. Dyer, P., McReynolds, S.: The Computational Theory of Optimal Control. Academic Press, New York (1970)
10. Flash, T., Hogan, N.: The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience 5, 1688-1703 (1985)
11. Grebenstein, M., van der Smagt, P.: Antagonism for a highly anthropomorphic hand-arm system. Advanced Robotics 22(1), 39-55 (2008)
12. Jacobson, D.H., Mayne, D.Q.: Differential Dynamic Programming. Elsevier, New York (1970)
13. Katayama, M., Kawato, M.: Virtual trajectory and stiffness ellipse during multijoint arm movement predicted by neural inverse model. Biological Cybernetics 69, 353-362 (1993)
14. Klanke, S., Vijayakumar, S., Schaal, S.: A library for locally weighted projection regression. Journal of Machine Learning Research 9, 623-626 (2008)
15. Li,W.: Optimal Control for Biological Movement Systems. PhD dissertation, University of California, San Diego (2006)
16. Li, W., Todorov, E.: Iterative linear-quadratic regulator design for nonlinear biological movement systems. In: Proc. 1st Int. Conf. Informatics in Control, Automation and Robotics (2004)
17. Li, W., Todorov, E.: Iterative linearization methods for approximately optimal control and estimation of non-linear stochastic system. International Journal of Control 80(9), 14391-14453 (2007)
18. Nguyen-Tuong, D., Peters, J., Seeger, M., Schoelkopf, B.: Computed torque control with nonparametric regressions techniques. In: American Control Conference (2008)
19. Özkaya, N., Nordin, M.: Fundamentals of biomechanics: equilibrium, motion, and deformation. Van Nostrand Reinhold, New York (1991)
20. Schaal, S.: Learning Robot Control. In: The handbook of brain theory and neural networks, pp. 983-987. MIT Press, Cambridge (2002)
21. Shadmehr, R., Mussa-Ivaldi, F.A.: Adaptive representation of dynamics during learning of a motor task. The Journal of Neurosciene 14(5), 3208-3224 (1994)
22. Shadmehr, R., Wise, S.P.: The Computational Neurobiology of Reaching and Ponting. MIT Press, Cambridge (2005)
23. Stengel, R.F.: Optimal control and estimation. Dover Publications, New York (1994)
24. Thrun, S.: Monte carlo POMDPs. In: Advances in Neural Information Processing Systems (NIPS), pp. 1064-1070 (2000)
25. Todorov, E.: Optimality principles in sensorimotor control. Nature Neuroscience 7(9), 907-915 (2004)
26. Todorov, E., Jordan, M.: Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5, 1226-1235 (2002)
27. Todorov, E., Jordan, M.: A minimal intervention principle for coordinated movement. In: Advances in Neural Information Processing Systems (NIPS), pp. 27-34. MIT Press, Cambridge (2003)
28. Todorov, E., Li, W.: A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. In: Proc. of the American Control Conference (2005)
29. Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectories in human multijoint arm movements: minimum torque-change model. Biological Cybernetics 61, 89-101 (1989)
30. Vijayakumar, S., D'Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Computation 17, 2602-2634 (2005)
31. Vijayakumar, S., D'Souza, A., Shibata, T., Conradt, J., Schaal, S.: Statistical learning for humanoid robots. Autonomous Robots 12(1), 55-69 (2002)
32. Wolf, S., Hirzinger, G.: A new variable stiffness design: Matching requirements of the next robot generation. In: Proc. Int. Conf. on Robotics and Automation (ICRA), pp. 1741-1746 (2008)