4 papers
Model-Agnostic Meta Learning for Differentiable MPC
Salma Elfeki, Riccardo Zuliani, Niklas Schmid +2
Applying policy optimization to Model Predictive Control (MPC) yields high-performance and reliable controllers. However, the resulting controllers often overfit their training con…
Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
Jiaqi Yan, Ankush Chakrabarty, Niklas Schmid +2
In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often ch…
Maximizing Reach-Avoid Probabilities for Linear Stochastic Systems via Control Architectures
Niklas Schmid, Jaeyoun Choi, Oswin So +1
The maximization of reach-avoid probabilities for stochastic systems is a central topic in the control literature. Yet, the available methods are either restricted to low-dimension…
Parameter-Robust MPPI for Safe Online Learning of Unknown Parameters
Matti Vahs, Jaeyoun Choi, Niklas Schmid +2
Robots deployed in dynamic environments must remain safe even when key physical parameters are uncertain or change over time. We propose Parameter-Robust Model Predictive Path Inte…