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& Bizzi 1982), when simulating fingers flexing and extending (Dennerlein, Diao, Mote
& Rempel 1998) or when simulating leg movements during gait (Olney, Griffin, Monga
& McBride 1991). Similarly, the number of independently controlled muscles is often
reduced (An, Chiao, Cooney & Linscheid 1985) for simplicity, or even made equal to the
number of kinematic degrees-of-freedom to avoid muscle redundancy (Harding, Brandt
& Hillberry 1993). While reducing the dimensionality of a model can be valid in many
occasions, one needs to be careful to ensure it is capable of replicating the function being
studied. For example, an inappropriate kinematic model can lead to erroneous predictions
(Valero-Cuevas, Towles & Hentz 2000), (Jinha, Ait-Haddou, Binding & Herzog 2006),
or reducing a set of muscles too severely may not be sufficiently realistic for clinical
purposes. A subtle but equally important risk is that of assembling a kinematic model
with a given number of degrees of freedom, but then not considering the full kinematic
output. For example, a three-joint planar linkage system to simulate a leg or a finger
has three kinematic DOF at the input, and also three kinematic degrees of freedom
at the output: the x and y location of the endpoint plus the orientation of the third
link. As a rule, the number of rotational degrees- of-freedom (i.e., joint angles) maps
into as many kinematic degrees-of-freedom at the endpoint (Murray, Li & Sastry 1994).
Thus, for example, studying muscle coordination to study endpoint location with- out
considering the orientation of the terminal link can lead to variable results. As we have
described in the literature (Valero-Cuevas, Zajac & Burgar 1998), (Valero-Cuevas 2009),
the geometric model and Jacobian of the linkage system need to account for all input and
output kinematic degrees- of-freedom to properly represent the mapping from muscle
actions to limb kinematics and kinetics.
230
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 244 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | & Bizzi 1982), when simulating fingers flexing and extending (Dennerlein, Diao, Mote & Rempel 1998) or when simulating leg movements during gait (Olney, Griffin, Monga & McBride 1991). Similarly, the number of independently controlled muscles is often reduced (An, Chiao, Cooney & Linscheid 1985) for simplicity, or even made equal to the number of kinematic degrees-of-freedom to avoid muscle redundancy (Harding, Brandt & Hillberry 1993). While reducing the dimensionality of a model can be valid in many occasions, one needs to be careful to ensure it is capable of replicating the function being studied. For example, an inappropriate kinematic model can lead to erroneous predictions (Valero-Cuevas, Towles & Hentz 2000), (Jinha, Ait-Haddou, Binding & Herzog 2006), or reducing a set of muscles too severely may not be sufficiently realistic for clinical purposes. A subtle but equally important risk is that of assembling a kinematic model with a given number of degrees of freedom, but then not considering the full kinematic output. For example, a three-joint planar linkage system to simulate a leg or a finger has three kinematic DOF at the input, and also three kinematic degrees of freedom at the output: the x and y location of the endpoint plus the orientation of the third link. As a rule, the number of rotational degrees- of-freedom (i.e., joint angles) maps into as many kinematic degrees-of-freedom at the endpoint (Murray, Li & Sastry 1994). Thus, for example, studying muscle coordination to study endpoint location with- out considering the orientation of the terminal link can lead to variable results. As we have described in the literature (Valero-Cuevas, Zajac & Burgar 1998), (Valero-Cuevas 2009), the geometric model and Jacobian of the linkage system need to account for all input and output kinematic degrees- of-freedom to properly represent the mapping from muscle actions to limb kinematics and kinetics. 230 |
