1 code implementation • 11 Jan 2024 • Dominik Baumann, Thomas B. Schön
In this work, we drop this assumption and show how we can perform safe learning when we cannot directly measure the context variables.
1 code implementation • 17 Oct 2023 • Dominik Baumann, Erfaun Noorani, James Price, Ole Peters, Colm Connaughton, Thomas B. Schön
The expected value is the average over the statistical ensemble of infinitely many trajectories.
no code implementations • 7 Sep 2023 • Dominik Baumann, Krzysztof Kowalczyk, Koen Tiels, Paweł Wachel
Unfortunately, Gaussian process inference scales cubically with the number of data points, limiting applicability to high-dimensional and embedded systems.
no code implementations • 15 May 2023 • Lukas Kesper, Sebastian Trimpe, Dominik Baumann
Model-free learning of communication and control policies provides an alternative.
no code implementations • 2 Apr 2023 • Dominik Baumann, Thomas B. Schön
In this article, we discuss, for a specific example, when the additional complexity of event-based methods is beneficial.
no code implementations • 30 Sep 2022 • Alexander Gräfe, Dominik Baumann, Sebastian Trimpe
We propose a fault detection method that uses these priorities to detect errors in other agents.
1 code implementation • 12 Feb 2022 • Sebastian Weichwald, Søren Wengel Mogensen, Tabitha Edith Lee, Dominik Baumann, Oliver Kroemer, Isabelle Guyon, Sebastian Trimpe, Jonas Peters, Niklas Pfister
Questions in causality, control, and reinforcement learning go beyond the classical machine learning task of prediction under i. i. d.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • 24 Jan 2022 • Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann
Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.
1 code implementation • 27 May 2021 • Dominik Baumann, Alonso Marco, Matteo Turchetta, Sebastian Trimpe
When learning policies for robotic systems from data, safety is a major concern, as violation of safety constraints may cause hardware damage.
no code implementations • 16 Apr 2021 • Fabian Mager, Dominik Baumann, Carsten Herrmann, Sebastian Trimpe, Marco Zimmerling
An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network.
1 code implementation • 16 Oct 2020 • Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe
We consider failing behaviors as those that violate a constraint and address the problem of learning with crash constraints, where no data is obtained upon constraint violation.
1 code implementation • 11 Aug 2020 • Niklas Funk, Dominik Baumann, Vincent Berenz, Sebastian Trimpe
We present a framework for model-free learning of event-triggered control strategies.
1 code implementation • 6 Jun 2020 • Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, Sebastian Trimpe
In this paper, we propose a method that identifies the causal structure of control systems.
1 code implementation • 15 May 2020 • Alonso Marco, Alexander von Rohr, Dominik Baumann, José Miguel Hernández-Lobato, Sebastian Trimpe
When learning to ride a bike, a child falls down a number of times before achieving the first success.
no code implementations • 23 Apr 2020 • Friedrich Solowjow, Dominik Baumann, Christian Fiedler, Andreas Jocham, Thomas Seel, Sebastian Trimpe
Evaluating whether data streams are drawn from the same distribution is at the heart of various machine learning problems.
no code implementations • 24 Jul 2019 • Alonso Marco, Dominik Baumann, Philipp Hennig, Sebastian Trimpe
Learning robot controllers by minimizing a black-box objective cost using Bayesian optimization (BO) can be time-consuming and challenging.
1 code implementation • 13 Sep 2018 • Dominik Baumann, Jia-Jie Zhu, Georg Martius, Sebastian Trimpe
Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods.
no code implementations • 5 Mar 2018 • Friedrich Solowjow, Dominik Baumann, Jochen Garcke, Sebastian Trimpe
Common event-triggered state estimation (ETSE) algorithms save communication in networked control systems by predicting agents' behavior, and transmitting updates only when the predictions deviate significantly.