Armin Lederer | DeepAI

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∙ 07/10/2023

Episodic Gaussian Process-Based Learning Control with Vanishing Tracking Errors

Due to the increasing complexity of technical systems, accurate first pr...

0 Armin Lederer, et al. ∙

∙ 05/25/2023

Koopman Kernel Regression

Many machine learning approaches for decision making, such as reinforcem...

0 Petar Bevanda, et al. ∙

∙ 05/14/2023

Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning

When the dynamics of systems are unknown, supervised machine learning te...

0 Xiaobing Dai, et al. ∙

∙ 03/31/2023

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States

As control engineering methods are applied to increasingly complex syste...

0 Robert Lefringhausen, et al. ∙

∙ 12/01/2022

Safe Learning-Based Control of Elastic Joint Robots via Control Barrier Functions

Ensuring safety is of paramount importance in physical human-robot inter...

0 Armin Lederer, et al. ∙

∙ 09/14/2022

Uncertainty-Aware Visual Perception for Safe Motion Planning

For safe operation, a robot must be able to avoid collisions in uncertai...

0 Ralf Römer, et al. ∙

∙ 07/04/2022

Safe Reinforcement Learning via Confidence-Based Filters

Ensuring safety is a crucial challenge when deploying reinforcement lear...

0 Sebastian Curi, et al. ∙

∙ 02/23/2022

Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes

Safety-critical technical systems operating in unknown environments requ...

0 Armin Lederer, et al. ∙

∙ 12/08/2021

KoopmanizingFlows: Diffeomorphically Learning Stable Koopman Operators

We propose a novel framework for constructing linear time-invariant (LTI...

0 Petar Bevanda, et al. ∙

∙ 11/05/2021

Adaptive Low-Pass Filtering using Sliding Window Gaussian Processes

When signals are measured through physical sensors, they are perturbed b...

0 Alejandro J. Ordóñez-Conejo, et al. ∙

∙ 10/01/2021

Personalized Rehabilitation Robotics based on Online Learning Control

The use of rehabilitation robotics in clinical applications gains increa...

0 Samuel Tesfazgi, et al. ∙

∙ 09/06/2021

Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications

Gaussian processes have become a promising tool for various safety-criti...

0 Alexandre Capone, et al. ∙

∙ 04/09/2021

Inverse Reinforcement Learning a Control Lyapunov Approach

Inferring the intent of an intelligent agent from demonstrations and sub...

0 Samuel Tesfazgi, et al. ∙

∙ 04/09/2021

Distributed Bayesian Online Learning for Cooperative Manipulation

For tasks where the dynamics of multiple agents are physically coupled, ...

0 Pablo Budde gen. Dohmann, et al. ∙

∙ 01/13/2021

Uniform Error and Posterior Variance Bounds for Gaussian Process Regression with Application to Safe Control

In application areas where data generation is expensive, Gaussian proces...

0 Armin Lederer, et al. ∙

∙ 11/20/2020

The Value of Data in Learning-Based Control for Training Subset Selection

Despite the existence of formal guarantees for learning-based control ap...

0 Armin Lederer, et al. ∙

∙ 10/06/2020

Real-time Uncertainty Decomposition for Online Learning Control

Safety-critical decisions based on machine learning models require a cle...

0 Jonas Umlauft, et al. ∙

∙ 06/16/2020

Real-Time Regression with Dividing Local Gaussian Processes

The increased demand for online prediction and the growing availability ...

0 Armin Lederer, et al. ∙

∙ 06/14/2020

GP3: A Sampling-based Analysis Framework for Gaussian Processes

Although machine learning is increasingly applied in control approaches,...

0 Armin Lederer, et al. ∙

∙ 06/14/2020

Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

Modelling real world systems involving humans such as biological process...

0 Wenxin Xiao, et al. ∙

∙ 05/25/2020

How Training Data Impacts Performance in Learning-based Control

When first principle models cannot be derived due to the complexity of t...

0 Armin Lederer, et al. ∙

∙ 05/04/2020

Localized active learning of Gaussian process state space models

The performance of learning-based control techniques crucially depends o...

0 Alexandre Capone, et al. ∙

∙ 06/04/2019

Posterior Variance Analysis of Gaussian Processes with Application to Average Learning Curves

The posterior variance of Gaussian processes is a valuable measure of th...

0 Armin Lederer, et al. ∙

∙ 06/04/2019

Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control

Data-driven models are subject to model errors due to limited and noisy ...

0 Armin Lederer, et al. ∙

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