Search Results for author:
Found 8 papers, 6 papers with code
Event-Triggered Time-Varying Bayesian Optimization
The results demonstrate that ET-GP-UCB is readily applicable without prior knowledge on the rate of change.
Improving the Performance of Robust Control through Event-Triggered Learning
However, in practice, many systems also exhibit uncertainty in the form of changes over time, e. g., due to weight shifts or wear and tear, leading to decreased performance or instability of the learning-based controller.
On Controller Tuning with Time-Varying Bayesian Optimization
Current TVBO methods do not explicitly account for these properties, resulting in poor tuning performance and many unstable controllers through over-exploration of the parameter space.
Local policy search with Bayesian optimization
We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
Probabilistic Robust Linear Quadratic Regulators with Gaussian Processes
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
When learning to ride a bike, a child falls down a number of times before achieving the first success.
A Learnable Safety Measure
While safety can only be guaranteed after learning the safety measure, we show that failures can already be greatly reduced by using the estimated measure during learning.
Gait learning for soft microrobots controlled by light fields
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits.
