Fast planning of well conditioned trajectories for model learning

Cong Wang, Yu Zhao, Chung Yen Lin, Masayoshi Tomizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIROS 2014 Conference Digest - IEEE/RSJ International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1460-1465
Number of pages6
ISBN (Electronic)9781479969340
DOIs
StatePublished - Oct 31 2014
Externally publishedYes
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago, United States
Duration: Sep 14 2014Sep 18 2014

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
CountryUnited States
CityChicago
Period9/14/149/18/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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