Towards Robot Skill Learning: From Simple Skills to Table Tennis

Peters, J., Kober, J., Muelling, K., Kroemer, O. and Neumann, G.
Conference Paper, Proceedings of the European Conference on Machine Learning (ECML), Nectar Track, September, 2013


Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

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.


@conference{Peters-2013-107886,
author = {Peters, J. and Kober, J. and Muelling, K. and Kroemer, O. and Neumann, G.},
title = {Towards Robot Skill Learning: From Simple Skills to Table Tennis},
booktitle = {Proceedings of the European Conference on Machine Learning (ECML), Nectar Track},
year = {2013},
month = {September},
} 2018-10-03T14:30:47-04:00