NDLI: Learning GP-BayesFilters via Gaussian process latent variable models

Content Provider	Springer Nature Link
Author	Ko, Jonathan Fox, Dieter
Copyright Year	2010
Abstract	GP-BayesFilters are a general framework for integrating Gaussian process prediction and observation models into Bayesian filtering techniques, including particle filters and extended and unscented Kalman filters. GP-BayesFilters have been shown to be extremely well suited for systems for which accurate parametric models are difficult to obtain. GP-BayesFilters learn non-parametric models from training data containing sequences of control inputs, observations, and ground truth states. The need for ground truth states limits the applicability of GP-BayesFilters to systems for which the ground truth can be estimated without significant overhead. In this paper we introduce GPBF-Learn, a framework for training GP-BayesFilters without ground truth states. Our approach extends Gaussian Process Latent Variable Models to the setting of dynamical robotics systems. We show how weak labels for the ground truth states can be incorporated into the GPBF-Learn framework. The approach is evaluated using a difficult tracking task, namely tracking a slotcar based on inertial measurement unit (IMU) observations only. We also show some special features enabled by this framework, including time alignment, and control replay for both the slotcar, and a robotic arm.
Starting Page	3
Ending Page	23
Page Count	21
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	30
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2010-10-26
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gaussian process System identification Bayesian filtering Time alignment System control Machine learning Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control , Robotics, Mechatronics Robotics and Automation
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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