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Learning to control in operational space

2008

Article

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ei


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.

Author(s): Peters, J. and Schaal, S.
Journal: International Journal of Robotics Research
Volume: 27
Pages: 197-212
Year: 2008

Department(s): Autonomous Motion, Empirical Inference
Bibtex Type: Article (article)

Cross Ref: p10235
DOI: 10.1177/0278364907087548
Note: clmc
URL: http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf

BibTex

@article{Peters_IJRR_2008,
  title = {Learning to control in operational space},
  author = {Peters, J. and Schaal, S.},
  journal = {International Journal of Robotics Research},
  volume = {27},
  pages = {197-212},
  year = {2008},
  note = {clmc},
  crossref = {p10235},
  url = {http://www-clmc.usc.edu/publications/P/peters-IJRR2008.pdf}
}