Page 16 |
Save page Remove page | Previous | 16 of 289 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
Despite all this evolution, learning for a robot how to autonomously perform human-like
motor control tasks such as object manipulation, walking, running etc, remains an
open problem. There is a combination of characteristics in humanoid robots which is
unique and it does not often exist in other cases of dynamical systems. These systems are
usually high dimensional. Depending on how many degrees of freedom are considered,
their dimensionality can easily exceed 100 states. Moreover their dynamical model is
usually unknown and hard estimate. In cases where a model is available, it is an ap-proximation
of the real dynamics, especially if one considers contact phenomena with the
environment as well as the various sources of stochasticity such as sensor and actuation
noise. Therefore, there is a level of uncertainty in humanoid robotic systems which is
structural and parametric, because it results from the lack of accurate dynamical models,
as well as stochastic due to noisy and imperfect sensors.
All these characteristics of humanoid robots open the question of how humans resolve
these issues due to the fact that they also perform motor control tasks in stochastic
environments and deal with contact phenomena and sensor noise. As for the characteristic
of dimensionality, this is also present in an even more pronounced way in bio-mechanical
systems. It suffices to realize that just for the control of the hand there are up to 30
actuated tendons.
Motivated by all these issues and difficulties, this thesis proposes a new stochastic op-timal
control formalism based on the framework of path integral control, which extends to
optics of robot learning and reinforcement learning. Path integral control framework and
its extensions to iterative optimal control are the central topic sof this thesis. Moreover,
inspired by the mystery of bio-mechanical motor control of the index finger, this thesis
2
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 16 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | Despite all this evolution, learning for a robot how to autonomously perform human-like motor control tasks such as object manipulation, walking, running etc, remains an open problem. There is a combination of characteristics in humanoid robots which is unique and it does not often exist in other cases of dynamical systems. These systems are usually high dimensional. Depending on how many degrees of freedom are considered, their dimensionality can easily exceed 100 states. Moreover their dynamical model is usually unknown and hard estimate. In cases where a model is available, it is an ap-proximation of the real dynamics, especially if one considers contact phenomena with the environment as well as the various sources of stochasticity such as sensor and actuation noise. Therefore, there is a level of uncertainty in humanoid robotic systems which is structural and parametric, because it results from the lack of accurate dynamical models, as well as stochastic due to noisy and imperfect sensors. All these characteristics of humanoid robots open the question of how humans resolve these issues due to the fact that they also perform motor control tasks in stochastic environments and deal with contact phenomena and sensor noise. As for the characteristic of dimensionality, this is also present in an even more pronounced way in bio-mechanical systems. It suffices to realize that just for the control of the hand there are up to 30 actuated tendons. Motivated by all these issues and difficulties, this thesis proposes a new stochastic op-timal control formalism based on the framework of path integral control, which extends to optics of robot learning and reinforcement learning. Path integral control framework and its extensions to iterative optimal control are the central topic sof this thesis. Moreover, inspired by the mystery of bio-mechanical motor control of the index finger, this thesis 2 |
