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Learning to control in operational space





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


  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}