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| Record Number | 10235 |
| Reference Type | Journal Article |
| Author(s) | Peters, J.;Schaal, S. |
| Year | 2008 |
| Title | Learning to control in operational space |
| Journal/Conference/Book Title | International Journal of Robotics Research |
| Keywords | operational space control, learning, em algorithm, redundancy resolution, reinforcement learning |
Abstract |
One of the most general frameworks for phrasing control problems for
complex, redundant robots is operational space control. However, while
this framework is of essential importance for robotics and well-understood
from an analytical point of view, it can be prohibitively hard to achieve
accurate control in face of modeling errors, which are inevitable in com-
plex robots, e.g., humanoid robots. In this paper, we suggest a learning
approach for opertional space control as a direct inverse model learning
problem. A first important insight for this paper is that a physically cor-
rect solution to the inverse problem with redundant degrees-of-freedom
does exist when learning of the inverse map is performed in a suitable
piecewise linear way. The second crucial component for our work is based
on the insight that many operational space controllers can be understood
in terms of a constrained optimal control problem. The cost function as-
sociated with this optimal control problem allows us to formulate a learn-
ing algorithm that automatically synthesizes a globally consistent desired
resolution of redundancy while learning the operational space controller.
From the machine learning point of view, this learning problem corre-
sponds to a reinforcement learning problem that maximizes an immediate
reward. We employ an expectation-maximization policy search algorithm
in order to solve this problem. Evaluations on a three degrees of freedom
robot arm are used to illustrate the suggested approach. The applica-
tion to a physically realistic simulator of the anthropomorphic SARCOS
Master arm demonstrates feasibility for complex high degree-of-freedom
robots. We also show that the proposed method works in the setting of
learning resolved motion rate control on real, physical Mitsubishi PA-10
medical robotics arm.
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| Notes | clmc |
| URL(s) | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf
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| Volume | 27 |
| Pages | 197-212 |
| Short Title | Learning to control in operational space |
| Papers are available as Adobe PDF ".pdf" files. Adobe Reader is available for free for all computer platforms.
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Page last modified on August 10, 2006, at 06:47 PM
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