Learning Sequential Motor Tasks

Christian Daniel, Gerhard Neumann, Oliver Kroemer and Jan Peters
Conference Paper, International Conference on Robotics and Automation (ICRA), January, 2013

Download Publication

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

Many real robot applications require the sequential use of multiple distinct motor primitives. This requirement implies the need to learn the individual primitives as well as a strategy to select the primitives sequentially. Such hierarchical learning problems are commonly either treated as one complex monolithic problem which is hard to learn, or as separate tasks learned in isolation. However, there exists a strong link between the robots strategy and its motor primitives. Consequently, a consistent framework is needed that can learn jointly on the level of the individual primitives and the robots strategy. We present a hierarchical learning method which improves individual motor primitives and, simultaneously, learns how to combine these motor primitives sequentially to solve complex motor tasks. We evaluate our method on the game of robot hockey, which is both difficult to learn in terms of the required motor primitives as well as its strategic elements.


@conference{Daniel-2013-112211,
author = {Christian Daniel and Gerhard Neumann and Oliver Kroemer and Jan Peters},
title = {Learning Sequential Motor Tasks},
booktitle = {International Conference on Robotics and Automation (ICRA)},
year = {2013},
month = {January},
} 2019-03-12T14:02:59-04:00