16 citations · 73 across the 34 of their papers we have counts for
8 papers · 1 filter
Diffeomorphically Learning Stable Koopman Operators
Petar Bevanda, Max Beier, Sebastian Kerz +3
System representations inspired by the infinite-dimensional Koopman operator (generator) are increasingly considered for predictive modeling. Due to the operator's linearity, a ran…
Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes
Alejandro J. Ordóñez-Conejo, Armin Lederer, Sandra Hirche
When signals are measured through physical sensors, they are perturbed by noise. To reduce noise, low-pass filters are commonly employed in order to attenuate high frequency compon…
Personalized Rehabilitation Robotics based on Online Learning Control
Samuel Tesfazgi, Armin Lederer, Johannes F. Kunz +2
The use of rehabilitation robotics in clinical applications gains increasing importance, due to therapeutic benefits and the ability to alleviate labor-intensive works. However, th…
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications
Alexandre Capone, Armin Lederer, Sandra Hirche
Gaussian processes have become a promising tool for various safety-critical settings, since the posterior variance can be used to directly estimate the model error and quantify ris…
Inverse Reinforcement Learning: A Control Lyapunov Approach
Samuel Tesfazgi, Armin Lederer, Sandra Hirche
Inferring the intent of an intelligent agent from demonstrations and subsequently predicting its behavior, is a critical task in many collaborative settings. A common approach to s…
Distributed Bayesian Online Learning for Cooperative Manipulation
Pablo Budde gen. Dohmann, Armin Lederer, Marcel Dißemond +1
For tasks where the dynamics of multiple agents are physically coupled, e.g., in cooperative manipulation, the coordination between the individual agents becomes crucial, which req…