Sebastian Trimpe

research

∙ 09/06/2023

Learning Hybrid Dynamics Models With Simulator-Informed Latent States

Dynamics model learning deals with the task of inferring unknown dynamic...

0 Katharina Ensinger, et al. ∙

research

∙ 09/05/2023

Exact Inference for Continuous-Time Gaussian Process Dynamics

Physical systems can often be described via a continuous-time dynamical ...

0 Katharina Ensinger, et al. ∙

research

∙ 08/11/2023

Scale-Preserving Automatic Concept Extraction (SPACE)

Convolutional Neural Networks (CNN) have become a common choice for indu...

0 Andres Felipe Posada-Moreno, et al. ∙

research

∙ 07/16/2023

Finite element inspired networks: Learning physically-plausible deformable object dynamics from partial observations

The accurate simulation of deformable linear object (DLO) dynamics is ch...

0 Shamil Mamedov, et al. ∙

research

∙ 06/29/2023

Experience Transfer for Robust Direct Data-Driven Control

Learning-based control uses data to design efficient controllers for spe...

0 Alexander von Rohr, et al. ∙

research

∙ 06/06/2023

Scalable Concept Extraction in Industry 4.0

The industry 4.0 is leveraging digital technologies and machine learning...

0 Andres Felipe Posada-Moreno, et al. ∙

research

∙ 04/19/2023

Approximate non-linear model predictive control with safety-augmented neural networks

Model predictive control (MPC) achieves stability and constraint satisfa...

0 Henrik Hose, et al. ∙

research

∙ 03/08/2023

Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test

Machine learning and deep learning have been used extensively to classif...

0 Behnam Khojasteh, et al. ∙

research

∙ 02/27/2023

Combining Slow and Fast: Complementary Filtering for Dynamics Learning

Modeling an unknown dynamical system is crucial in order to predict the ...

0 Katharina Ensinger, et al. ∙

research

∙ 02/23/2023

Data-Driven Observability Analysis for Nonlinear Stochastic Systems

Distinguishability and, by extension, observability are key properties o...

0 Pierre-François Massiani, et al. ∙

research

∙ 08/23/2022

Event-Triggered Time-Varying Bayesian Optimization

We consider the problem of sequentially optimizing a time-varying object...

0 Paul Brunzema, et al. ∙

research

∙ 08/03/2022

Learning Fast and Precise Pixel-to-Torque Control

In the field, robots often need to operate in unknown and unstructured e...

0 Steffen Bleher, et al. ∙

research

∙ 07/28/2022

Improving the Performance of Robust Control through Event-Triggered Learning

Robust controllers ensure stability in feedback loops designed under unc...

0 Alexander von Rohr, et al. ∙

research

∙ 07/22/2022

On Controller Tuning with Time-Varying Bayesian Optimization

Changing conditions or environments can cause system dynamics to vary ov...

0 Paul Brunzema, et al. ∙

research

∙ 07/14/2022

The Wheelbot: A Jumping Reaction Wheel Unicycle

Combining off-the-shelf components with 3Dprinting, the Wheelbot is a sy...

0 A. Rene Geist, et al. ∙

research

∙ 06/09/2022

ECLAD: Extracting Concepts with Local Aggregated Descriptors

Convolutional neural networks are being increasingly used in critical sy...

0 Andres Felipe Posada-Moreno, et al. ∙

research

∙ 05/25/2022

Learning dynamics from partial observations with structured neural ODEs

Identifying dynamical systems from experimental data is a notably diffic...

0 Mona Buisson-Fenet, et al. ∙

research

∙ 02/12/2022

Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning

Questions in causality, control, and reinforcement learning go beyond th...

0 Sebastian Weichwald, et al. ∙

research

∙ 01/24/2022

Scalable Safe Exploration for Global Optimization of Dynamical Systems

Learning optimal control policies directly on physical systems is challe...

0 Bhavya Sukhija, et al. ∙

research

∙ 06/22/2021

Local policy search with Bayesian optimization

Reinforcement learning (RL) aims to find an optimal policy by interactio...

0 Sarah Müller, et al. ∙

research

∙ 05/27/2021

GoSafe: Globally Optimal Safe Robot Learning

When learning policies for robotic systems from data, safety is a major ...

0 Dominik Baumann, et al. ∙

research

∙ 05/25/2021

Safe Value Functions

The relationship between safety and optimality in control is not well un...

10 Pierre-François Massiani, et al. ∙

research

∙ 05/17/2021

Probabilistic robust linear quadratic regulators with Gaussian processes

Probabilistic models such as Gaussian processes (GPs) are powerful tools...

0 Alexander von Rohr, et al. ∙

research

∙ 05/07/2021

Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees

The combination of machine learning with control offers many opportuniti...

0 Christian Fiedler, et al. ∙

research

∙ 05/06/2021

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

Gaussian Process Regression is a popular nonparametric regression method...

0 Christian Fiedler, et al. ∙

research

∙ 04/16/2021

Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload

An important class of cyber-physical systems relies on multiple agents t...

0 Fabian Mager, et al. ∙

research

∙ 02/02/2021

Symplectic Gaussian Process Dynamics

Dynamics model learning is challenging and at the same time an active fi...

0 Katharina Ensinger, et al. ∙

research

∙ 12/11/2020

Structured learning of rigid-body dynamics: A survey and unified view

Accurate models of mechanical system dynamics are often critical for mod...

0 A. Rene Geist, et al. ∙

research

∙ 10/18/2020

Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges

Smart manufacturing aims to overcome the limitations of today's rigid as...

0 Dominik Baumann, et al. ∙

research

∙ 10/16/2020

Robot Learning with Crash Constraints

In the past decade, numerous machine learning algorithms have been shown...

0 Alonso Marco, et al. ∙

research

∙ 08/11/2020

Learning Event-triggered Control from Data through Joint Optimization

We present a framework for model-free learning of event-triggered contro...

0 Niklas Funk, et al. ∙

research

∙ 06/06/2020

Identifying Causal Structure in Dynamical Systems

We present a method for automatically identifying the causal structure o...

0 Dominik Baumann, et al. ∙

research

∙ 05/15/2020

Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

When learning to ride a bike, a child falls down a number of times befor...

0 Alonso Marco, et al. ∙

research

∙ 04/23/2020

Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes

The identification of the constrained dynamics of mechanical systems is ...

0 A. Rene Geist, et al. ∙

research

∙ 04/23/2020

A Kernel Two-sample Test for Dynamical Systems

A kernel two-sample test is developed for deciding whether two dynamical...

0 Friedrich Solowjow, et al. ∙

research

∙ 12/22/2019

Safe and Fast Tracking Control on a Robot Manipulator: Robust MPC and Neural Network Control

Fast feedback control and safety guarantees are essential in modern robo...

0 Julian Nubert, et al. ∙

research

∙ 11/22/2019

Actively Learning Gaussian Process Dynamics

Despite the availability of ever more data enabled through modern sensor...

0 Mona Buisson-Fenet, et al. ∙

research

∙ 10/29/2019

Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

In reinforcement learning (RL), an autonomous agent learns to perform co...

10 Matteo Turchetta, et al. ∙

research

∙ 10/07/2019

A Learnable Safety Measure

Failures are challenging for learning to control physical systems since ...

0 Steve Heim, et al. ∙

research

∙ 09/19/2019

Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees

Closing feedback loops fast and over long distances is key to emerging c...

0 Dominik Baumann, et al. ∙

research

∙ 07/24/2019

Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties

Learning robot controllers by minimizing a black-box objective cost usin...

3 Alonso Marco, et al. ∙

research

∙ 06/13/2019

Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems

This abstract describes the first public demonstration of feedback contr...

0 Fabian Mager, et al. ∙

research

∙ 06/08/2019

Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

In future autonomous systems, wireless multi-hop communication is key to...

0 Dominik Baumann, et al. ∙

research

∙ 05/14/2019

Trajectory-Based Off-Policy Deep Reinforcement Learning

Policy gradient methods are powerful reinforcement learning algorithms a...

5 Andreas Doerr, et al. ∙

research

∙ 01/19/2019

Resource-aware IoT Control: Saving Communication through Predictive Triggering

The Internet of Things (IoT) interconnects multiple physical devices in ...

0 Sebastian Trimpe, et al. ∙

research

∙ 12/15/2018

Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

Bayesian optimization is proposed for automatic learning of optimal cont...

0 Matthias Neumann-Brosig, et al. ∙

research

∙ 09/13/2018

Deep Reinforcement Learning for Event-Triggered Control

Event-triggered control (ETC) methods can achieve high-performance contr...

0 Dominik Baumann, et al. ∙

research

∙ 09/10/2018

Gait learning for soft microrobots controlled by light fields

Soft microrobots based on photoresponsive materials and controlled by li...

0 Alexander von Rohr, et al. ∙

research

∙ 06/11/2018

Learning an Approximate Model Predictive Controller with Guarantees

A supervised learning framework is proposed to approximate a model predi...

0 Michael Hertneck, et al. ∙

research

∙ 05/27/2018

A Local Information Criterion for Dynamical Systems

Encoding a sequence of observations is an essential task with many appli...

0 Arash Mehrjou, et al. ∙

Sebastian Trimpe

Featured Co-authors