114 citations · 408 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
Virtual vs. Real: Trading Off Simulations and Physical Experiments in Reinforcement Learning with Bayesian Optimization
Alonso Marco, Felix Berkenkamp, Philipp Hennig +4
In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to aut…
Automatic LQR Tuning Based on Gaussian Process Global Optimization
Alonso Marco, Philipp Hennig, Jeannette Bohg +2
This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller…