TR2022-066
Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models
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- , "Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models", American Control Conference (ACC), DOI: 10.23919/ACC53348.2022.9867635, June 2022, pp. 940-945.BibTeX TR2022-066 PDF
- @inproceedings{Berntorp2022jun,
- author = {Berntorp, Karl and Menner, Marcel},
- title = {Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models},
- booktitle = {American Control Conference (ACC)},
- year = 2022,
- pages = {940--945},
- month = jun,
- doi = {10.23919/ACC53348.2022.9867635},
- url = {https://www.merl.com/publications/TR2022-066}
- }
- , "Online Constrained Bayesian Inference and Learning of Gaussian-Process State-Space Models", American Control Conference (ACC), DOI: 10.23919/ACC53348.2022.9867635, June 2022, pp. 940-945.
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MERL Contacts:
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Research Areas:
Abstract:
Recent research has shown that it is possible to perform online learning of nonlinear dynamical systems. Furthermore, the results suggest that combining approximate Gaussian-process (GP) regression with model-based estimators, such as Kalman filters and particle filters (PFs), leads to efficient learners under the GP-state-space model (GP-SSM) framework. Here, we analyze how learning of GP-SSMs can be done when there are constraints on the system to be learned. Our analysis is based on a recently developed online PF-based learning method, where the GP-SSM is expressed as a basis-function expansion. We show that the method by adaptation of the basis functions can satisfy several constraints, such as symmetry, antisymmetry, Neumann boundary conditions, and linear operator constraints. A Monte-Carlo simulation study indicates reduced estimation errors with more than 50%.
Related News & Events
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NEWS Karl Berntorp gave Spotlight Talk at CDC Workshop on Gaussian Process Learning-Based Control Date: December 5, 2022
Where: Cancun, Mexico
MERL Contact: Karl Berntorp
Research Areas: Control, Machine LearningBrief- Karl Berntorp was an invited speaker at the workshop on Gaussian Process Learning-Based Control organized at the Conference on Decision and Control (CDC) 2022 in Cancun, Mexico.
The talk was part of a tutorial-style workshop aimed to provide insight into the fundamentals behind Gaussian processes for modeling and control and sketching some of the open challenges and opportunities using Gaussian processes for modeling and control. The talk titled ``Gaussian Processes for Learning and Control: Opportunities for Real-World Impact" described some of MERL's efforts in using Gaussian processes (GPs) for learning and control, with several application examples and discussing some of the key benefits and limitations with using GPs for learning-based control.
- Karl Berntorp was an invited speaker at the workshop on Gaussian Process Learning-Based Control organized at the Conference on Decision and Control (CDC) 2022 in Cancun, Mexico.
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NEWS MERL researchers presented 9 papers at the American Control Conference (ACC) Date: June 8, 2022 - June 10, 2022
Where: Atlanta, GA
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Christopher R. Laughman; Marcel Menner; Rien Quirynen; Abraham P. Vinod; Avishai Weiss
Research Areas: Control, Machine Learning, OptimizationBrief- At the American Control Conference in Atlanta, GA, MERL presented 9 papers on subjects including autonomous-vehicle decision making and motion planning, realtime Bayesian inference and learning, reference governors for hybrid systems, Bayesian optimization, and nonlinear control.