17 citations · 17 across the 4 of their papers we have counts for
6 papers
Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control
Julius Jagdt, Johanna Menn, Sebastian Trimpe +2
Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety gu…
Inverse Optimal Control as an Errors-in-Variables Problem
Rahel Rickenbach, Anna Scampicchio, Melanie N. Zeilinger
Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence. However, truly optimal demonstrations are often difficult to…
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…
Active Learning-based Model Predictive Coverage Control
Rahel Rickenbach, Johannes Köhler, Anna Scampicchio +2
The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications. However, coverage applications face two major cha…
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…
Stable and Robust LQR Design via Scenario Approach
Anna Scampicchio, Aleksandr Aravkin, Gianluigi Pillonetto
Linear Quadratic Regulator (LQR) design is one of the most classical optimal control problems, whose well-known solution is an input sequence expressed as a state-feedback. In this…