24 citations · 47 across the 9 of their papers we have counts for
10 papers
Gaussian processes for dynamics learning in model predictive control
Anna Scampicchio, Elena Arcari, Amon Lahr +1
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dyn…
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
Rahel Rickenbach, Elena Arcari, Melanie N. Zeilinger
The choice of objective is critical for the performance of an optimal controller. When control requirements vary during operation, e.g. due to changes in the environment with which…
Error analysis of regularized trigonometric linear regression with unbounded sampling: a statistical learning viewpoint
Anna Scampicchio, Elena Arcari, Melanie N. Zeilinger
The effectiveness of non-parametric, kernel-based methods for function estimation comes at the price of high computational complexity, which hinders their applicability in adaptive…
Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation
Elena Arcari, Maria Vittoria Minniti, Anna Scampicchio +4
Mobile manipulation in robotics is challenging due to the need of solving many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-prin…
Stochastic MPC with robustness to bounded parametric uncertainty
Elena Arcari, Andrea Iannelli, Andrea Carron +1
The performance of model-based control techniques strongly depends on the quality of the employed dynamics model. If strong guarantees are desired, it is therefore common to robust…
Contextual Tuning of Model Predictive Control for Autonomous Racing
Lukas P. Fröhlich, Christian Küttel, Elena Arcari +3
Learning-based model predictive control has been widely applied in autonomous racing to improve the closed-loop behaviour of vehicles in a data-driven manner. When environmental co…
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Elena Arcari, Andrea Carron, Melanie N. Zeilinger
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus cont…
Dual Stochastic MPC for Systems with Parametric and Structural Uncertainty
Elena Arcari, Lukas Hewing, Max Schlichting +1
Designing controllers for systems affected by model uncertainty can prove to be a challenge, especially when seeking the optimal compromise between the conflicting goals of identif…
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Lukas Hewing, Elena Arcari, Lukas P. Fröhlich +1
Established techniques for simulation and prediction with Gaussian process (GP) dynamics often implicitly make use of an independence assumption on successive function evaluations…
An Approximate Dynamic Programming Approach for Dual Stochastic Model Predictive Control
Elena Arcari, Lukas Hewing, Melanie N. Zeilinger
Dual control explicitly addresses the problem of trading off active exploration and exploitation in the optimal control of partially unknown systems. While the problem can be cast…