14 citations · 40 across the 20 of their papers we have counts for
6 papers · 1 filter
Toward Near-Globally Optimal Nonlinear Model Predictive Control via Diffusion Models
Tzu-Yuan Huang, Armin Lederer, Nicolas Hoischen +4
Achieving global optimality in nonlinear model predictive control (NMPC) is challenging due to the non-convex nature of the underlying optimization problem. Since commonly employed…
Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Jan Brüdigam, Ali-Adeeb Abbas, Maks Sorokin +7
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks re…
Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
Nicolas Hoischen, Petar Bevanda, Stefan Sosnowski +2
This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where b…
Nonparametric Control Koopman Operators
Petar Bevanda, Bas Driessen, Lucian Cristian Iacob +3
This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictiona…
Learning-based Prescribed-Time Safety for Control of Unknown Systems with Control Barrier Functions
Tzu-Yuan Huang, Sihua Zhang, Xiaobing Dai +4
In many control system applications, state constraint satisfaction needs to be guaranteed within a prescribed time. While this issue has been partially addressed for systems with k…
Cooperative Learning with Gaussian Processes for Euler-Lagrange Systems Tracking Control under Switching Topologies
Zewen Yang, Songbo Dong, Armin Lederer +5
This work presents an innovative learning-based approach to tackle the tracking control problem of Euler-Lagrange multi-agent systems with partially unknown dynamics operating unde…