Multi-output Infinite Horizon Gaussian Processes

Jaehyun Lim, Jehyun Park, Sungjae Nah, Jongeun Choi

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


Learning the uncertain dynamical environments for online learning and prediction from noisy sensory measurement streams is essential for various tasks in robotics. Recently, Gaussian process (GP) online learning such as an infinite-horizon Gaussian process (IHGP) has shown effectiveness to cope with non-stationary dynamical random processes in learning hyperparameters online by reducing the computational cost. However, the IHGP was originally proposed to deal with only a single-output. Therefore, to tackle complex real-world problems, we propose a multi-output infinite-horizon Gaussian process (MOIHGP) that generalizes the single-output IHGP to deal with multiple outputs for better prediction. Our approach allows us to consider correlations between multiple outputs for better prediction, even with occlusions in a Bayesian way. Finally, we successfully demonstrate the effectiveness of our approach by benchmark and experimental results. For simulated benchmark experiments with high noise levels, our approach reduced 16.6% of the averaged RMSE value achieved by the single-output IHGP.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728190778
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: 2021 May 302021 Jun 5

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729


Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021

Bibliographical note

Funding Information:
*This work was supported by the Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry and Energy (10073129). *This work was also supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE)(20206610100290, Development of Work Safety Management Platform in Power Plant).

Publisher Copyright:
© 2021 IEEE

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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