114 citations · 408 across the 9 of their papers we have counts for
11 papers
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models
Alonso Marco, Elias Morley, Claire J. Tomlin
In order for robots to safely navigate in unseen scenarios using learning-based methods, it is important to accurately detect out-of-training-distribution (OoD) situations online.…
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…
Robot Learning with Crash Constraints
Alonso Marco, Dominik Baumann, Majid Khadiv +3
In the past decade, numerous machine learning algorithms have been shown to successfully learn optimal policies to control real robotic systems. However, it is common to encounter…
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Alonso Marco, Alexander von Rohr, Dominik Baumann +2
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a toler…
Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties
Alonso Marco, Dominik Baumann, Philipp Hennig +1
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging. It is very often the case that some roll…
Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study
Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann +1
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data. A probabilistic description (a Gaussian process) is used to model…