6 citations · 7 across the 7 of their papers we have counts for
9 papers
Learning Geometrically-Informed Lyapunov Functions with Deep Diffeomorphic RBF Networks
Samuel Tesfazgi, Leonhard Sprandl, Sandra Hirche
The practical deployment of learning-based autonomous systems would greatly benefit from tools that flexibly obtain safety guarantees in the form of certificate functions from data…
A Set-Theoretic Robust Control Approach for Linear Quadratic Games with Unknown Counterparts
Francesco Bianchin, Robert Lefringhausen, Elisa Gaetan +2
Ensuring robust decision-making in multi-agent systems is challenging when agents have distinct, possibly conflicting objectives and lack full knowledge of each other's strategies.…
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…
Vision-Based Uncertainty-Aware Motion Planning based on Probabilistic Semantic Segmentation
Ralf Römer, Armin Lederer, Samuel Tesfazgi +1
For safe operation, a robot must be able to avoid collisions in uncertain environments. Existing approaches for motion planning under uncertainties often assume parametric obstacle…
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes
Armin Lederer, Mingmin Zhang, Samuel Tesfazgi +1
Safety-critical technical systems operating in unknown environments require the ability to quickly adapt their behavior, which can be achieved in control by inferring a model onlin…