Gaussian process model based predictive control

By Jus Kocijan, Roderick Murray-Smith, Carl E. Rasmussen, Agathe Girard. 2004

287 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

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In Control and Systems Engineering

Citations in the corpus

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, methodology): “…such as Gaussian processes (GPs), are often the model of choice in MBRL, especially in low-dimensional problems where data efficiency is critical [Kocijan et al., 2004, Ko et al., 2007, Nguyen-Tuong et al., 2008, Grancharova et al., 2008, Deisenroth et al., 2014, Kamthe and Deisenroth, 2018].”
DeepMPC: Learning Deep Latent Features for Model Predictive Control (2015, background): “Thus, several recent works attempt to learn more general dynamics models such as Gaussian mixture models [7, 21] and Gaussian processes [22].”
Dynamics-Aware Unsupervised Discovery of Skills (2019, methodology): “…has been traditionally approached with methods that are well-suited for low-data regimes such as Gaussian Processes (Rasmussen, 2003) showing signiﬁcant data-efﬁciency gains over model-free approaches (Deisenroth et al., 2013; Kamthe & Deisenroth, 2017; Kocijan et al., 2004; Ko et al., 2007).”
Deep predictive policy training using reinforcement learning (2017, background): “More recently, MPC is accompanied by general function approximators, such as Gaussian processes [14], [24] or artificial neural networks [10] to learn the dynamic model directly from interaction data.”
Learning-Based Model Predictive Control: Toward Safe Learning in Control (2020, background): “…of GP-based MPC to robotic systems have been presented, e.g., for trajectory tracking with robotic manipulators (95), path following of off-road mobile robots (87, 96), autonomous racing (86, 90, 97), process control (98), and telescope systems in the context of periodic additive disturbances (99).”
Model learning for robot control: a survey (2011, methodology): “In more recent work, probabilistic methods are employed to learn such multi-step prediction models (Girard et al. 2002; Kocijan et al. 2004).”
Learning-Based Model Predictive Control for Safe Exploration (2018, methodology): “MPC based on Gaussian process (GP, [8]) models is proposed in a number of works, e.g. [9], [10].”
Variational Bayesian learning of nonlinear hidden state-space models for model predictive control (2009, methodology): “Similar model based predictive control has been done with Gaussian processes in [16] where the inputs of the mapping include the previous observations and control signals up to a given lag.”
Stability of Controllers for Gaussian Process Forward Models (2016, unknown): “Many successful applications impressively demonstrate the efficiency of this approach (Deisenroth et al., 2015; Engel et al., 2006; Pan & Theodorou, 2014; Kocijan et al., 2004; Klenske et al., 2013; Maciejowski & Yang, 2013; Nguyen-Tuong & Peters, 2011).”
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning (2019, unknown): “E.g., authors used Linear Regression (Schaal et al., 2002; Haruno et al., 2001), Gaussian Mixture Regression (Calinon et al., 2010; Khansari-Zadeh & Billard, 2011), Gaussian Process Regression (Kocijan et al., 2004; Nguyen-Tuong et al., 2009; Nguyen-Tuong & Peters, 2010), Support Vector Regression (”