Towards Robot Skill Learning: From Simple Skills to Table Tennis
Abstract
Learning robots that can acquire new motor skills and refine existing ones have been a long-standing vision of both robotics, and machine learning. However, off-the-shelf machine learning appears not to be adequate for robot skill learning, as it neither scales to anthropomorphic robotics nor do fulfills the crucial real-time requirements. As an alternative, we propose to divide the generic skill learning problem into parts that can be well-understood from a robotics point of view. In this context, we have developed machine learning methods applicable to robot skill learning. This paper discusses recent progress ranging from simple skill learning problems to a game of robot table tennis.
Cite
Text
Peters et al. "Towards Robot Skill Learning: From Simple Skills to Table Tennis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40994-3_42Markdown
[Peters et al. "Towards Robot Skill Learning: From Simple Skills to Table Tennis." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/peters2013ecmlpkdd-robot/) doi:10.1007/978-3-642-40994-3_42BibTeX
@inproceedings{peters2013ecmlpkdd-robot,
title = {{Towards Robot Skill Learning: From Simple Skills to Table Tennis}},
author = {Peters, Jan and Kober, Jens and Mülling, Katharina and Krömer, Oliver and Neumann, Gerhard},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {627-631},
doi = {10.1007/978-3-642-40994-3_42},
url = {https://mlanthology.org/ecmlpkdd/2013/peters2013ecmlpkdd-robot/}
}