Jonas Umlauft — accepted AI-conference papers

2021

Gaussian Process-Based Real-Time Learning for Safety Critical Applications

Armin Lederer, Alejandro J Ordóñez Conejo, Korbinian A Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche

ICML 2021spotlight

The safe operation of physical systems typically relies on high-quality models. Since a continuous stream of data is generated during run-time, such models are often obtained through the application of Gaussian process regression because it provides guarantees on the prediction error. Due to its hig…

Cited by 50 Source ↗PDF ↗Scholar ↗

2019

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

Armin Lederer, Jonas Umlauft, Sandra Hirche

NeurIPS 2019poster

Data-driven models are subject to model errors due to limited and noisy training data. Key to the application of such models in safety-critical domains is the quantification of their model error. Gaussian processes provide such a measure and uniform error bounds have been derived, which allow safe c…

Cited by 201 Source ↗PDF ↗Scholar ↗

2017

Bayesian uncertainty modeling for programming by demonstration

Jonas Umlauft, Yunis Fanger, Sandra Hirche

ICRA 2017poster

Programming by Demonstration allows to transfer skills from human demonstrators to robotic systems by observation and reproduction. One aspect that is often overlooked is that humans show different trajectories over multiple demonstrations for the same task. Observed movements may be more precise in…

Cited by 19 Source ↗Scholar ↗

2017

Learning Stable Stochastic Nonlinear Dynamical Systems

Jonas Umlauft, Sandra Hirche

ICML 2017poster

A data-driven identification of dynamical systems requiring only minimal prior knowledge is promising whenever no analytically derived model structure is available, e.g., from first principles in physics. However, meta-knowledge on the system’s behavior is often given and should be exploited: Stabil…

Cited by 31 Source ↗PDF ↗Scholar ↗

2016

Gaussian processes for dynamic movement primitives with application in knowledge-based cooperation

Yunis Fanger, Jonas Umlauft, Sandra Hirche

IROS 2016poster

Dynamic Movement Primitives (DMPs) represent stable goal-directed or periodic movements, which are learned from observations or demonstrations. They rely on proper function approximators, which are sufficiently flexible to represent arbitrary movements but also ensure goal convergence in point-to-po…

Cited by 31 Source ↗Scholar ↗