23 citations · 52 across the 10 of their papers we have counts for
10 papers
A Decade of Bayesian Optimization for Controller Tuning and Robot Learning: Tutorial, Review, and Future Prospects
David Stenger, Paul Brunzema, Johanna Menn +3
In the past decade, Bayesian optimization (BO) has emerged as a powerful and adaptable framework for automatic controller tuning and robot learning. This article offers a comprehen…
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
Mingxuan Che, Tsung-Yuan Tseng, Theresa Eimer +2
Reinforcement learning (RL) has shown remarkable success across a wide range of complex tasks. However, RL outcomes can be highly stochastic, and both expected performance and vari…
Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization
Pierre-François Massiani, Alexander von Rohr, Lukas Haverbeck +1
Despite the many recent advances in reinforcement learning (RL), the question of learning policies that robustly satisfy state constraints under unknown disturbances remains open.…
Synergistic Action and Style Priors for Learning Legged Locomotion
Oliver Hausdörfer, Alexander von Rohr, Éric Lefort +1
Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unrestricted nature of the learning algori…
Robust Direct Data-Driven Control for Probabilistic Systems
Alexander von Rohr, Dmitrii Likhachev, Sebastian Trimpe
We propose a data-driven control method for systems with aleatoric uncertainty, for example, robot fleets with variations between agents. Our method leverages shared trajectory dat…
Event-Triggered Time-Varying Bayesian Optimization
Paul Brunzema, Alexander von Rohr, Friedrich Solowjow +1
We consider the problem of sequentially optimizing a time-varying objective function using time-varying Bayesian optimization (TVBO). Current approaches to TVBO require prior knowl…
Improving the Performance of Robust Control through Event-Triggered Learning
Alexander von Rohr, Friedrich Solowjow, Sebastian Trimpe
Robust controllers ensure stability in feedback loops designed under uncertainty but at the cost of performance. Model uncertainty in time-invariant systems can be reduced by recen…
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…
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…
Gait learning for soft microrobots controlled by light fields
Alexander von Rohr, Sebastian Trimpe, Alonso Marco +2
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits. This inherent flexibility can be exploited to maximize…