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Found 12 papers, 0 papers with code
Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors
We address this issue by deriving a Bayesian prediction error bound for GP regression, which we show to decay with the growth of a novel, kernel-based measure of data density.
Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control
In application areas where data generation is expensive, Gaussian processes are a preferred supervised learning model due to their high data-efficiency.
The Impact of Data on the Stability of Learning-Based Control- Extended Version
In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance.
Deep Learning based Uncertainty Decomposition for Real-time Control
Data-driven control in unknown environments requires a clear understanding of the involved uncertainties for ensuring safety and efficient exploration.
Real-Time Regression with Dividing Local Gaussian Processes
The increased demand for online prediction and the growing availability of large data sets drives the need for computationally efficient models.
Smart Forgetting for Safe Online Learning with Gaussian Processes
The identification of unknown dynamical systems using supervised learning enables model-based control of systems that cannot be modeled based on first principles.
How Training Data Impacts Performance in Learning-based Control
When first principle models cannot be derived due to the complexity of the real system, data-driven methods allow us to build models from system observations.
Localized active learning of Gaussian process state space models
We apply the proposed method to explore the state space of various dynamical systems and compare our approach to a commonly used entropy-based exploration strategy.
Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves
The posterior variance of Gaussian processes is a valuable measure of the learning error which is exploited in various applications such as safe reinforcement learning and control design.
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
Finally, we derive safety conditions for the control of unknown dynamical systems based on Gaussian process models and evaluate them in simulations of a robotic manipulator.
Mean Square Prediction Error of Misspecified Gaussian Process Models
A naturally provided model confidence is highly relevant for system-theoretical considerations to provide guarantees for application scenarios.
Learning Stable Stochastic Nonlinear Dynamical Systems
A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e. g., from first principles in physics.
