16 citations · 73 across the 34 of their papers we have counts for
5 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…
Safe Event-triggered Gaussian Process Learning for Barrier-Constrained Control
Armin Lederer, Azra Begzadić, Sandra Hirche +2
While control barrier functions (CBFs) are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical lim…
Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
Samuel Tesfazgi, Leonhard Sprandl, Armin Lederer +1
Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborativ…
Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes
Samuel Tesfazgi, Markus Keßler, Emilio Trigili +2
Ensuring safety and adapting to the user's behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot's mechanical…
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…