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 ï¬rst 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.
| Author(s): | Peters, J. and Schaal, S. |
| Journal: | International Journal of Robotics Research |
| Volume: | 27 |
| Pages: | 197-212 |
| Year: | 2008 |
| Bibtex Type: | Article (article) |
| DOI: | 10.1177/0278364907087548 |
| URL: | http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf |
| Cross Ref: | p10235 |
| Electronic Archiving: | grant_archive |
| Note: | clmc |
BibTex
@article{Peters_IJRR_2008,
title = {Learning to control in operational space},
journal = {International Journal of Robotics Research},
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 ï¬rst 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. },
volume = {27},
pages = {197-212},
year = {2008},
note = {clmc},
slug = {peters_ijrr_2008},
author = {Peters, J. and Schaal, S.},
crossref = {p10235},
url = {http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf}
}