258 citations · 1.2k across the 64 of their papers we have counts for
7 papers · 1 filter
Local policy search with Bayesian optimization
Sarah Müller, Alexander von Rohr, Sebastian Trimpe
Reinforcement learning (RL) aims to find an optimal policy by interaction with an environment. Consequently, learning complex behavior requires a vast number of samples, which can…
GoSafe: Globally Optimal Safe Robot Learning
Dominik Baumann, Alonso Marco, Matteo Turchetta +1
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage. SafeOpt is an efficient Bayesian opti…
On exploration requirements for learning safety constraints
Pierre-François Massiani, Steve Heim, Sebastian Trimpe
Enforcing safety for dynamical systems is challenging, since it requires constraint satisfaction along trajectory predictions. Equivalent control constraints can be computed in the…
Safe Value Functions
Pierre-François Massiani, Steve Heim, Friedrich Solowjow +1
Safety constraints and optimality are important, but sometimes conflicting criteria for controllers. Although these criteria are often solved separately with different tools to mai…
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Alexander von Rohr, Matthias Neumann-Brosig, Sebastian Trimpe
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. While learning-based con…
Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload
Fabian Mager, Dominik Baumann, Carsten Herrmann +2
An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network. Examples include self-driv…
Structure-preserving Gaussian Process Dynamics
Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche +2
Most physical processes posses structural properties such as constant energies, volumes, and other invariants over time. When learning models of such dynamical systems, it is criti…