Learning GP-BayesFilters via Gaussian process latent variable models

By Jonathan Ko, Dieter Fox. 2009

108 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Signal Processing

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Citations in the corpus

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016, background): “Estimating probabilistic models for sequential data is central to many domains, such as audio, natural language or physical plants [7, 19, 3, 4, 13].”
Hilbert Space Embeddings of Predictive State Representations (2013, neutral): “([25, 20, 10] used a similar dataset.”
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning (2011, methodology): “In recent years, GP dynamics models were more often used for learning robot dynamics [9, 10, 14].”
Gaussian Processes for Data-Efficient Learning in Robotics and Control (2015, neutral): “For a more general POMDP set-up, Gaussian Process Dynamical Models (GPDMs) [29], [54] could be used for”
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation (2015, methodology): “This approach is motivated by the ‘‘dynamic window approach’’ of Fox et al. (1997).”
Bootstrapping bilinear models of Simple Vehicles (2015, methodology): “D(Y;X;U; ∆) is the set of all discrete-time dynamical systems with sampling interval ∆.”
Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression (2014, methodology): “Details of our specific data collection procedure can be found in Section V.”
Predictive State Recurrent Neural Networks (2017, methodology): “[17] use subspace ID to initialize EM for linear dyanmical systems, and Ko and Fox [18] use N4SID [19] to initialize GP-Bayes filters.”
GP-BayesFilters: Bayesian filtering using Gaussian process prediction and observation models (2008, background): “They are thus well suited for cases with uncertain training data, and their extension to learning GPBayesFilters has recently been investigated with extremely promising results (Ko and Fox 2009).”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, methodology): “Early variants of the GPSSM were learned by finding the maximum a posteriori (MAP) estimates of the latent states, generating various successful positioning applications, among others the RSS-based WiFi localization [100], the human motion capture [101], and the IMU-based slotcar tracking [102], etc”