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05/15/2023
Learning Switching Port-Hamiltonian Systems with Uncertainty Quantification
Switching physical systems are ubiquitous in modern control applications...
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05/15/2023
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior
Data-driven approaches achieve remarkable results for the modeling of co...
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05/15/2023
Physics-enhanced Gaussian Process Variational Autoencoder
Variational autoencoders allow to learn a lower-dimensional latent space...
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11/20/2020
The Value of Data in Learning-Based Control for Training Subset Selection
Despite the existence of formal guarantees for learning-based control ap...
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10/06/2020
Real-time Uncertainty Decomposition for Online Learning Control
Safety-critical decisions based on machine learning models require a cle...
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09/14/2020
Safe learning-based trajectory tracking for underactuated vehicles with partially unknown dynamics
Underactuated vehicles have gained much attention in the recent years du...
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06/25/2020
Prediction with Gaussian Process Dynamical Models
The modeling and simulation of dynamical systems is a necessary step for...
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05/04/2020
Localized active learning of Gaussian process state space models
The performance of learning-based control techniques crucially depends o...
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09/12/2019
Closed-loop Model Selection for Kernel-based Models using Bayesian Optimization
Kernel-based nonparametric models have become very attractive for model-...
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06/25/2019
Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control
Tracking control for soft robots is challenging due to uncertainties in ...
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11/16/2018
Mean Square Prediction Error of Misspecified Gaussian Process Models
Nonparametric modeling approaches show very promising results in the are...
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06/19/2018