72 citations · 322 across the 23 of their papers we have counts for
24 papers
Simulation-Aided Policy Tuning for Black-Box Robot Learning
Shiming He, Alexander von Rohr, Dominik Baumann +2
How can robots learn and adapt to new tasks and situations with little data? Systematic exploration and simulation are crucial tools for efficient robot learning. We present a nove…
Toward Multi-Agent Reinforcement Learning for Distributed Event-Triggered Control
Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Event-triggered communication and control provide high control performance in networked control systems without overloading the communication network. However, most approaches requ…
On the trade-off between event-based and periodic state estimation under bandwidth constraints
Dominik Baumann, Thomas B. Schön
Event-based methods carefully select when to transmit information to enable high-performance control and estimation over resource-constrained communication networks. However, they…
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…
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems
Bhavya Sukhija, Matteo Turchetta, David Lindner +3
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage. Most existing model-free learning meth…