5 citations · 6 across the 3 of their papers we have counts for
3 papers
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…
Local Bayesian Optimization for Controller Tuning with Crash Constraints
Alexander von Rohr, David Stenger, Dominik Scheurenberg +1
Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method…
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…