2 citations · 3 across the 14 of their papers we have counts for
3 papers · 1 filter
Real-Time Online Learning for Model Predictive Control using a Spatio-Temporal Gaussian Process Approximation
Lars Bartels, Amon Lahr, Andrea Carron +1
Learning-based model predictive control (MPC) can enhance control performance by correcting for model inaccuracies, enabling more precise state trajectory predictions than traditio…
Graph Neural Model Predictive Control for High-Dimensional Systems
Patrick Benito Eberhard, Luis Pabon, Daniele Gammelli +5
The control of high-dimensional systems, such as soft robots, requires models that faithfully capture complex dynamics while remaining computationally tractable. This work presents…
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Amon Lahr, Anna Scampicchio, Johannes Köhler +1
Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler for safe learning-based control. I…