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Buchli, J., Theodorou, E., Stulp, F. & Schaal, S. (2010), Variable impedance control -
a reinforcement learning approach, in ‘Robotics: Science and Systems Conference
(RSS)’.
Cerveri, P., De Momi, E., Marchente, M., Lopomo, N., Baud-Bovy, G., Barros, R. M. L.
& Ferrigno, G. (2008), ‘In vivo validation of a realistic kinematic model for the
trapezio-metacarpal joint using an optoelectronic system’, ANNALS OF BIOMED-ICAL
ENGINEERING 36(7), 1268–1280.
Cheng, G., Hyon, S., Morimoto, J., Ude, A., Hale, J., Colvin, G., Scroggin, W. & Jacob-sen,
S. C. (2007), ‘Cb: A humanoid research platform for exploring neuroscience’,
Journal of Advanced Robotics 21(10), 1097–1114.
Chirikjian, S. G. (2009), Stochastic Models, Information Theory, and Lie Groups., Vol. I,
Birkh˝auser.
Dayan, P. & Hinton, G. (1997), ‘Using em for reinforcement learning’, Neural Computa-tion
9.
Deisenroth, M. P., Rasmussen, C. E. & Peters, J. (2009), ‘Gaussian process dynamic
programming’, Neurocomputing 72(7–9), 1508–1524.
Delp, S. L. & Loan, J. P. (2007), ‘A graphics-based software system to develop and
analyze models of musculoskeletal structures,’, Computers in Biology and Medicine
25(1), 21 – 34.
Dennerlein, J. T., Diao, E., Mote, C. D. & Rempel, D. M. (1998), ‘Tensions of the
flexor digitorum superficialis are higher than a current model predicts’, Journal of
Biomechanics 31(4), 295 – 301.
Dorato, P., Cerone, V. & Abdallah, C. (2000), Linear Quadratic Control: An Introduc-tion,
Krieger Publishing Co., Inc., Melbourne, FL, USA.
Doyle, J. (1978), ‘Guaranteed margins for lqg regulators’, Automatic Control, IEEE
Transactions on 23(4), 756 – 757.
Esteki, A. & Mansour, J. M. (1996), ‘An experimentally based nonlinear viscoelastic
model of joint passive moment’, Journal of Biomechanics 29(4), 443 – 450.
Feynman, P. R. & Hibbs, A. (2005), Quantum Mechanics and Path Integrals, Dover -
(Emended Edition).
Fleming, W. H. & Soner, H. M. (2006), Controlled Markov Processes and Viscosity Solu-tions,
Applications of aathematics, 2nd edn, Springer, New York.
Freivalds, A. (2000), Biomechanics of the upper limbs: mechanics, modeling, and Muscu-loskeletal
injures, 1rd edn, CRC Press.
Friedman, A. (1975), Stochastic Differential Equations And Applications, Academic Press.
269
Object Description
| Title | Iterative path integral stochastic optimal control: theory and applications to motor control |
| Author | Theodorou, Evangelos A. |
| Author email | etheodor@usc.edu; theo0027@umn.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2011-01-11 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-04-29 |
| Advisor (committee chair) | Schaal, Stefan |
| Advisor (committee member) |
Valero-Cuevas, Francisco Sukhatme, Gaurav S. Todorov, Emo Schweighofer, Nicolas |
| Abstract | Motivated by the limitations of current optimal control and reinforcement learning methods in terms of their efficiency and scalability, this thesis proposes an iterative stochastic optimal control approach based on the generalized path integral formalism. More precisely, we suggest the use of the framework of stochastic optimal control with path integrals to derive a novel approach to RL with parameterized policies. While solidly grounded in value function estimation and optimal control based on the stochastic Hamilton Jacobi Bellman (HJB) equation, policy improvements can be transformed into an approximation problem of a path integral which has no open algorithmic parameters other than the exploration noise. The resulting algorithm can be conceived of as model-based, semi-model-based, or even model free, depending on how the learning problem is structured. The new algorithm, Policy Improvement with Path Integrals (PI2), demonstrates interesting similarities with previous RL research in the framework of probability matching and provides intuition why the slightly heuristically motivated probability matching approach can actually perform well. Applications to high dimensional robotic systems are presented for a variety of tasks that require optimal planning and gain scheduling.; In addition to the work on generalized path integral stochastic optimal control, in this thesis we extend model based iterative optimal control algorithms to the stochastic setting. More precisely we derive the Differential Dynamic Programming algorithm for stochastic systems with state and control multiplicative noise. Finally, in the last part of this thesis, model based iterative optimal control methods are applied to bio-mechanical models of the index finger with the goal to find the underlying tendon forces applied for the movements of, tapping and flexing. |
| Keyword | stochastic optimal control; reinforcement learning,; robotics |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3804 |
| Contributing entity | University of Southern California |
| Rights | Theodorou, Evangelos A. |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Theodorou-4581 |
| Archival file | uscthesesreloadpub_Volume14/etd-Theodorou-4581.pdf |
Description
| Title | Page 283 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | Buchli, J., Theodorou, E., Stulp, F. & Schaal, S. (2010), Variable impedance control - a reinforcement learning approach, in ‘Robotics: Science and Systems Conference (RSS)’. Cerveri, P., De Momi, E., Marchente, M., Lopomo, N., Baud-Bovy, G., Barros, R. M. L. & Ferrigno, G. (2008), ‘In vivo validation of a realistic kinematic model for the trapezio-metacarpal joint using an optoelectronic system’, ANNALS OF BIOMED-ICAL ENGINEERING 36(7), 1268–1280. Cheng, G., Hyon, S., Morimoto, J., Ude, A., Hale, J., Colvin, G., Scroggin, W. & Jacob-sen, S. C. (2007), ‘Cb: A humanoid research platform for exploring neuroscience’, Journal of Advanced Robotics 21(10), 1097–1114. Chirikjian, S. G. (2009), Stochastic Models, Information Theory, and Lie Groups., Vol. I, Birkh˝auser. Dayan, P. & Hinton, G. (1997), ‘Using em for reinforcement learning’, Neural Computa-tion 9. Deisenroth, M. P., Rasmussen, C. E. & Peters, J. (2009), ‘Gaussian process dynamic programming’, Neurocomputing 72(7–9), 1508–1524. Delp, S. L. & Loan, J. P. (2007), ‘A graphics-based software system to develop and analyze models of musculoskeletal structures,’, Computers in Biology and Medicine 25(1), 21 – 34. Dennerlein, J. T., Diao, E., Mote, C. D. & Rempel, D. M. (1998), ‘Tensions of the flexor digitorum superficialis are higher than a current model predicts’, Journal of Biomechanics 31(4), 295 – 301. Dorato, P., Cerone, V. & Abdallah, C. (2000), Linear Quadratic Control: An Introduc-tion, Krieger Publishing Co., Inc., Melbourne, FL, USA. Doyle, J. (1978), ‘Guaranteed margins for lqg regulators’, Automatic Control, IEEE Transactions on 23(4), 756 – 757. Esteki, A. & Mansour, J. M. (1996), ‘An experimentally based nonlinear viscoelastic model of joint passive moment’, Journal of Biomechanics 29(4), 443 – 450. Feynman, P. R. & Hibbs, A. (2005), Quantum Mechanics and Path Integrals, Dover - (Emended Edition). Fleming, W. H. & Soner, H. M. (2006), Controlled Markov Processes and Viscosity Solu-tions, Applications of aathematics, 2nd edn, Springer, New York. Freivalds, A. (2000), Biomechanics of the upper limbs: mechanics, modeling, and Muscu-loskeletal injures, 1rd edn, CRC Press. Friedman, A. (1975), Stochastic Differential Equations And Applications, Academic Press. 269 |
