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Chapter 8
Control of the index finger
In this chapter we apply the iterative optimal control algorithm on two bio-mechanical
models of the index finger and we compare the resulting behavior. The bio-mechanical
models share the same multi-body dynamics but they differ in the tendon geometry since
they incorporate different moment arm matrices found in (Valero-Cuevas et al. 1998) and
(An, Ueba, Chao, Cooney & Linscheid 1983). As it is illustrated, the different moment
arm matrices play important role in the actuation capabilities of each model of the index
finger which become obvious as we compare the underlying tension profiles for the case
a flexing and a tapping movement.
The remaining of this chapter is organized as follows: in section 8.1 we provide a short
introduction for the biomechanics of the index finger while in section 8.2 we discuss the
iterative linear quadratic regulator which is the optimal control algorithm used for our
simulations. In section 8.3 we provide the multi-body dynamics and in 8.4 we compare
our results on the optimal control of the index finger between the two models of the
moment arm matrices. The moment arm models and the optimal control algorithm are
tested on the tasks of flexing and tapping with the index finger.
236
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 250 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | Chapter 8 Control of the index finger In this chapter we apply the iterative optimal control algorithm on two bio-mechanical models of the index finger and we compare the resulting behavior. The bio-mechanical models share the same multi-body dynamics but they differ in the tendon geometry since they incorporate different moment arm matrices found in (Valero-Cuevas et al. 1998) and (An, Ueba, Chao, Cooney & Linscheid 1983). As it is illustrated, the different moment arm matrices play important role in the actuation capabilities of each model of the index finger which become obvious as we compare the underlying tension profiles for the case a flexing and a tapping movement. The remaining of this chapter is organized as follows: in section 8.1 we provide a short introduction for the biomechanics of the index finger while in section 8.2 we discuss the iterative linear quadratic regulator which is the optimal control algorithm used for our simulations. In section 8.3 we provide the multi-body dynamics and in 8.4 we compare our results on the optimal control of the index finger between the two models of the moment arm matrices. The moment arm models and the optimal control algorithm are tested on the tasks of flexing and tapping with the index finger. 236 |
