258 citations · 1.2k across the 64 of their papers we have counts for
13 papers · 1 filter
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Julian Nubert, Johannes Köhler, Vincent Berenz +2
Fast feedback control and safety guarantees are essential in modern robotics. We present an approach that achieves both by combining novel robust model predictive control (MPC) wit…
Actively Learning Gaussian Process Dynamics
Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient…
A Learnable Safety Measure
Steve Heim, Alexander von Rohr, Sebastian Trimpe +1
Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constr…
Event-triggered Learning for Linear Quadratic Control
Henning Schlüter, Friedrich Solowjow, Sebastian Trimpe
When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically…
Fast Feedback Control over Multi-hop Wireless Networks with Mode Changes and Stability Guarantees
Dominik Baumann, Fabian Mager, Romain Jacob +3
Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update interval…
Predictive Triggering for Distributed Control of Resource Constrained Multi-agent Systems
José Mario Mastrangelo, Dominik Baumann, Sebastian Trimpe
A predictive triggering (PT) framework for the distributed control of resource constrained multi-agent systems is proposed. By predicting future communication demands and deriving…
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Alonso Marco, Dominik Baumann, Philipp Hennig +1
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll…
Demo Abstract: Fast Feedback Control and Coordination with Mode Changes for Wireless Cyber-Physical Systems
Fabian Mager, Dominik Baumann, Romain Jacob +3
This abstract describes the first public demonstration of feedback control and coordination of multiple physical systems over a dynamic multi-hop low-power wireless network with up…
Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems
Dominik Baumann, Fabian Mager, Marco Zimmerling +1
In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to tr…
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
Jonas Beuchert, Friedrich Solowjow, Jörg Raisch +2
Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning methods reduce network communication by sending inf…
Event-triggered Learning
Friedrich Solowjow, Sebastian Trimpe
The efficient exchange of information is an essential aspect of intelligent collective behavior. Event-triggered control and estimation achieve some efficiency by replacing continu…
Event-triggered Pulse Control with Model Learning (if Necessary)
Dominik Baumann, Friedrich Solowjow, Karl Henrik Johansson +1
In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduc…
Resource-aware IoT Control: Saving Communication through Predictive Triggering
Sebastian Trimpe, Dominik Baumann
The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, fee…