230 citations · 244 across the 7 of their papers we have counts for
16 papers
Parameter Filter-based Event-triggered Learning
Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe
Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption - access to an accurate model of the system. In…
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning
Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee +6
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i.i.d. observations. Instead, these fields consider th…
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…
On exploration requirements for learning safety constraints
Pierre-François Massiani, Steve Heim, Sebastian Trimpe
Enforcing safety for dynamical systems is challenging, since it requires constraint satisfaction along trajectory predictions. Equivalent control constraints can be computed in the…
Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges
Dominik Baumann, Fabian Mager, Ulf Wetzker +3
Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. T…
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…