@inproceedings{eca466d69ea347b9882cba83b3bc3513,
title = "Fast planning of well conditioned trajectories for model learning",
abstract = "This paper discusses the problem of planning well conditioned trajectories for learning a class of nonlinear models such as the imaging model of a camera and the multibody dynamic model of a robot. In such model learning problems, the model parameters can be linearly decoupled from system variables in the feature space. The learning accuracy and robustness against measurement noise and unmodeled response depend largely on the condition number of the data matrix. A new method is proposed to plan well conditioned trajectories efficiently by using low-discrepancy sequences and matrix subset selection. Application examples show promising results.",
author = "Cong Wang and Yu Zhao and Lin, {Chung Yen} and Masayoshi Tomizuka",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 ; Conference date: 14-09-2014 Through 18-09-2014",
year = "2014",
month = oct,
day = "31",
doi = "10.1109/IROS.2014.6942749",
language = "English (US)",
series = "IEEE International Conference on Intelligent Robots and Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1460--1465",
booktitle = "IROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems",
address = "United States",
}