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corresponding tendons are flexing as expected. Correspondingly the tendon excur-sions
EC and EI for are moving outwards and thus operate as expected. Moreover
the tendons LUM, RI and UI move outwards as it is illustrated in the two figures.
• In figures 8.3,8.4 and 8.8,8.9 the tensions applied on the 7 tendons to generate
the flexing movement are shown. Clearly for the case of the first moment arm
there is a synchronized burst of activity since all the tensions are reaching their
maximum tensions during the time window between 0ms and 0.2 ms. For the case
of the second moment arm, the results in 8.8, do not illustrated a burst of activity
but they rather suggest a different mechanism which is characterized by a higher
tensions in the FDP tendon with respect to the rest tendons, and a delay in the
activation of the FDS and EI, EC tendons as it is shown in figure 8.9.
• The torque profiles are illustrated in figures 8.5 and 8.11. As it is illustrated the
torque profiles are very similar since in both cases the highest torque is generated
around the MCP join and the smallest around the DIP join. The torques applied
at the MCP and DIP join for the first moment arm reach a smaller pick than the
corresponding pick reached by MCP and DIP torques for the second moment arm
matrix. Furthermore the torques for the first moment arm 8.5 are changing over
time in smoother fashion than the torques in 8.11.
In the next subsection we will continue our sensitivity analysis for the case of the
tapping movement and we are testing again the two moment arm matrices.
252
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 266 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | corresponding tendons are flexing as expected. Correspondingly the tendon excur-sions EC and EI for are moving outwards and thus operate as expected. Moreover the tendons LUM, RI and UI move outwards as it is illustrated in the two figures. • In figures 8.3,8.4 and 8.8,8.9 the tensions applied on the 7 tendons to generate the flexing movement are shown. Clearly for the case of the first moment arm there is a synchronized burst of activity since all the tensions are reaching their maximum tensions during the time window between 0ms and 0.2 ms. For the case of the second moment arm, the results in 8.8, do not illustrated a burst of activity but they rather suggest a different mechanism which is characterized by a higher tensions in the FDP tendon with respect to the rest tendons, and a delay in the activation of the FDS and EI, EC tendons as it is shown in figure 8.9. • The torque profiles are illustrated in figures 8.5 and 8.11. As it is illustrated the torque profiles are very similar since in both cases the highest torque is generated around the MCP join and the smallest around the DIP join. The torques applied at the MCP and DIP join for the first moment arm reach a smaller pick than the corresponding pick reached by MCP and DIP torques for the second moment arm matrix. Furthermore the torques for the first moment arm 8.5 are changing over time in smoother fashion than the torques in 8.11. In the next subsection we will continue our sensitivity analysis for the case of the tapping movement and we are testing again the two moment arm matrices. 252 |
