9 citations · 31 across the 22 of their papers we have counts for
23 papers
Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control
Julius Jagdt, Johanna Menn, Sebastian Trimpe +2
Learning-based Model Predictive Control (MPC) using Gaussian processes (GPs) is an effective approach for safe control in the presence of model mismatch. High-probability safety gu…
WarpMPC: Large-Batch MPC on GPU via ADMM with Unrolled Factorization
Henrik Hose, Se Hwan Jeon, Charles Khazoom +2
This paper introduces numerical optimizations for maximizing throughput on GPU when solving large batches (10,000 to over 100,000) of sequential quadratic programming (SQP) iterati…
The Mini Wheelbot Dataset: High-Fidelity Data for Robot Learning
Henrik Hose, Paul Brunzema, Devdutt Subhasish +1
The development of robust learning-based control algorithms for unstable systems requires high-quality, real-world data, yet access to specialized robotic hardware remains a signif…
RockNet: Distributed Learning on Ultra-Low-Power Devices
Alexander Gräfe, Fabian Mager, Marco Zimmerling +1
As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (Tiny…
DMPC-Swarm: Distributed Model Predictive Control on Nano UAV Swarms
Alexander Gräfe, Joram Eickhoff, Marco Zimmerling +1
Swarms of unmanned aerial vehicles (UAVs) are increasingly becoming vital to our society, undertaking tasks such as search and rescue, surveillance and delivery. A special variant…
MPX: Mixed Precision Training for JAX
Alexander Gräfe, Sebastian Trimpe
Mixed-precision training has emerged as an indispensable tool for enhancing the efficiency of neural network training in recent years. Concurrently, JAX has grown in popularity as…
Event-triggered Robust Model Predictive Control under Hard Computation Resource Constraints
Alexander Gräfe, Sebastian Trimpe
Model predictive control (MPC) is capable of controlling nonlinear systems with guaranteed constraint satisfaction and stability. However, MPC requires solving optimization problem…
Diffusion-Based Approximate MPC: Fast and Consistent Imitation of Multi-Modal Action Distributions
Pau Marquez Julbe, Julian Nubert, Henrik Hose +2
Approximating model predictive control (MPC) using imitation learning (IL) allows for fast control without solving expensive optimization problems online. However, methods that use…
The Mini Wheelbot: A Testbed for Learning-based Balancing, Flips, and Articulated Driving
Henrik Hose, Jan Weisgerber, Sebastian Trimpe
The Mini Wheelbot is a balancing, reaction wheel unicycle robot designed as a testbed for learning-based control. It is an unstable system with highly nonlinear yaw dynamics, non-h…
Safety in safe Bayesian optimization and its ramifications for control
Christian Fiedler, Johanna Menn, Sebastian Trimpe
A recurring and important task in control engineering is parameter tuning under constraints, which conceptually amounts to optimization of a blackbox function accessible only throu…
Lipschitz Safe Bayesian Optimization for Automotive Control
Johanna Menn, Pietro Pelizzari, Michael Fleps-Dezasse +1
Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to aut…
Feedforward Controllers from Learned Dynamic Local Model Networks with Application to Excavator Assistance Functions
Leon Greiser, Ozan Demir, Benjamin Hartmann +2
Complicated first principles modelling and controller synthesis can be prohibitively slow and expensive for high-mix, low-volume products such as hydraulic excavators. Instead, in…
Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining
Henrik Hose, Alexander Gräfe, Sebastian Trimpe
Model Predictive Control (MPC) is a method to control nonlinear systems with guaranteed stability and constraint satisfaction but suffers from high computation times. Approximate M…
On Safety in Safe Bayesian Optimization
Christian Fiedler, Johanna Menn, Lukas Kreisköther +1
Optimizing an unknown function under safety constraints is a central task in robotics, biomedical engineering, and many other disciplines, and increasingly safe Bayesian Optimizati…
Automatic nonlinear MPC approximation with closed-loop guarantees
Abdullah Tokmak, Christian Fiedler, Melanie N. Zeilinger +2
Safety guarantees are vital in many control applications, such as robotics. Model predictive control (MPC) provides a constructive framework for controlling safety-critical systems…
On kernel-based statistical learning in the mean field limit
Christian Fiedler, Michael Herty, Sebastian Trimpe
In many applications of machine learning, a large number of variables are considered. Motivated by machine learning of interacting particle systems, we consider the situation when…
Approximate non-linear model predictive control with safety-augmented neural networks
Henrik Hose, Johannes Köhler, Melanie N. Zeilinger +1
Model predictive control (MPC) achieves stability and constraint satisfaction for general nonlinear systems, but requires computationally expensive online optimization. This paper…
Reproducing kernel Hilbert spaces in the mean field limit
Christian Fiedler, Michael Herty, Michael Rom +2
Kernel methods, being supported by a well-developed theory and coming with efficient algorithms, are among the most popular and successful machine learning techniques. From a mathe…
Towards remote fault detection by analyzing communication priorities
Alexander Gräfe, Dominik Baumann, Sebastian Trimpe
The ability to detect faults is an important safety feature for event-based multi-agent systems. In most existing algorithms, each agent tries to detect faults by checking its own…
Revisiting the derivation of stage costs in infinite horizon discrete-time optimal control
Christian Fiedler, Sebastian Trimpe
In many applications of optimal control, the stage cost is not fixed, but rather a design choice with considerable impact on the control performance. In infinite horizon optimal co…
Event-triggered and distributed model predictive control for guaranteed collision avoidance in UAV swarms
Alexander Gräfe, Joram Eickhoff, Sebastian Trimpe
Distributed model predictive control (DMPC) is often used to tackle path planning for unmanned aerial vehicle (UAV) swarms. However, it requires considerable computations on-board…
Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
The combination of machine learning with control offers many opportunities, in particular for robust control. However, due to strong safety and reliability requirements in many rea…
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression
Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estima…