Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)
Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.
| Author(s): | Peters, J. and Kober, J. and Schaal, S. |
| Book Title: | Automatisierungstechnik |
| Volume: | 58 |
| Number (issue): | 12 |
| Pages: | 688-694 |
| Year: | 2010 |
| Bibtex Type: | Article (article) |
| URL: | http://www-clmc.usc.edu/publications/P/peters-Auto2010.pdf |
| Cross Ref: | p10417 |
| Electronic Archiving: | grant_archive |
| Note: | clmc |
BibTex
@article{Peters_A_2010,
title = {Policy learning algorithmis for motor learning (Algorithmen zum automatischen Erlernen von Motorfähigkigkeiten)},
booktitle = {Automatisierungstechnik},
abstract = {Robot learning methods which allow au- tonomous robots to adapt to novel situations have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. However, to date, learning techniques have yet to ful- fill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics. If possible, scaling was usually only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general ap- proach policy learning with the goal of an application to motor skill refinement in order to get one step closer towards human- like performance. For doing so, we study two major components for such an approach, i. e., firstly, we study policy learning algo- rithms which can be applied in the general setting of motor skill learning, and, secondly, we study a theoretically well-founded general approach to representing the required control structu- res for task representation and execution.},
volume = {58},
number = {12},
pages = {688-694},
year = {2010},
note = {clmc},
slug = {peters_a_2010},
author = {Peters, J. and Kober, J. and Schaal, S.},
crossref = {p10417},
url = {http://www-clmc.usc.edu/publications/P/peters-Auto2010.pdf}
}