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motion and force during tapping. On the theoretical side the study in (Venkadesan &
Valero-Cuevas 2008a) has found that such transitions from motion to well-directed con-tact
force are a fundamental part of dexterous manipulation, and that such tasks are
likely controlled optimally. Moreover, one of the main assumptions in (Venkadesan &
Valero-Cuevas 2008a) is that the underlying control strategy of the finger is considered
to be open loop. In addition, the model used is a torque driven model while the neu-romuscular
delays are modeled as activation contraction dynamics at the level of the
torques driving the 3 joints of the index finger. Even though with this simple model
the optimality principles of the motion to force transition for the task of tapping were
investigated, an open loop control strategy would have failed in tasks such as object ma-nipulation
where feedback control is critical requirement for successfully performing the
manipulation task. Furthermore, since only 3 sets of differential equation that model the
activation contraction dynamics are considered, the full structure and redundancy of the
index finger is not explored and the system under investigation remains in nature torque
driven.
In this chapter we have reviewed previous work on bio-mechanical modeling by touch-ing
the critical issues of skeletal mechanics, muscle redundancy and musculotendon rout-ing
as well as on application of optimal control theory to psychophysical and neuromus-cular
models. We have provided the main differences between torque driven and tendon
driven systems. We have discussed the role of the use of control theory into bio-mechanical
models not only as a tool that provides insights regarding the underlying control strategies
put also as a way to verify bio-mechanical models through a sensitivity analysis.
234
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 248 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | motion and force during tapping. On the theoretical side the study in (Venkadesan & Valero-Cuevas 2008a) has found that such transitions from motion to well-directed con-tact force are a fundamental part of dexterous manipulation, and that such tasks are likely controlled optimally. Moreover, one of the main assumptions in (Venkadesan & Valero-Cuevas 2008a) is that the underlying control strategy of the finger is considered to be open loop. In addition, the model used is a torque driven model while the neu-romuscular delays are modeled as activation contraction dynamics at the level of the torques driving the 3 joints of the index finger. Even though with this simple model the optimality principles of the motion to force transition for the task of tapping were investigated, an open loop control strategy would have failed in tasks such as object ma-nipulation where feedback control is critical requirement for successfully performing the manipulation task. Furthermore, since only 3 sets of differential equation that model the activation contraction dynamics are considered, the full structure and redundancy of the index finger is not explored and the system under investigation remains in nature torque driven. In this chapter we have reviewed previous work on bio-mechanical modeling by touch-ing the critical issues of skeletal mechanics, muscle redundancy and musculotendon rout-ing as well as on application of optimal control theory to psychophysical and neuromus-cular models. We have provided the main differences between torque driven and tendon driven systems. We have discussed the role of the use of control theory into bio-mechanical models not only as a tool that provides insights regarding the underlying control strategies put also as a way to verify bio-mechanical models through a sensitivity analysis. 234 |
