no code implementations • 5 Apr 2022 • Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe
However, they usually come with a critical assumption - access to an accurate model of the system.
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.
no code implementations • 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 • NeurIPS 2021 • Sarah Müller, Alexander von Rohr, Sebastian Trimpe
We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
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 • 25 May 2021 • Pierre-François Massiani, Steve Heim, Friedrich Solowjow, Sebastian Trimpe
Safety constraints and optimality are important, but sometimes conflicting criteria for controllers.
1 code implementation • 17 May 2021 • Pierre-François Massiani, Steve Heim, Sebastian Trimpe
In particular, we discuss the family of constraints that enforce safety in the context of a nominal control policy, and expose that these constraints do not need to be accurate everywhere.
1 code implementation • 17 May 2021 • Alexander von Rohr, Matthias Neumann-Brosig, Sebastian Trimpe
Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design.
no code implementations • 7 May 2021 • Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
The combination of machine learning with control offers many opportunities, in particular for robust control.
no code implementations • 6 May 2021 • Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe
However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncertainty bounds are required.
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.
no code implementations • 2 Feb 2021 • Katharina Ensinger, Friedrich Solowjow, Sebastian Ziesche, Michael Tiemann, Sebastian Trimpe
On the other hand, classical numerical integrators are specifically designed to preserve these crucial properties through time.
no code implementations • 11 Dec 2020 • A. René Geist, Sebastian Trimpe
Accurate models of mechanical system dynamics are often critical for model-based control and reinforcement learning.
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 • L4DC 2020 • Andreas Geist, Sebastian Trimpe
The identification of the constrained dynamics of mechanical systems is often challenging.
1 code implementation • 6 Jun 2020 • Dominik Baumann, Friedrich Solowjow, Karl H. Johansson, Sebastian Trimpe
We present a method for automatically identifying the causal structure of a dynamical control system.
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 were generated by the same distribution is at the heart of many machine learning problems, e. g. to detect changes.
1 code implementation • 23 Apr 2020 • A. Rene Geist, Sebastian Trimpe
The identification of the constrained dynamics of mechanical systems is often challenging.
no code implementations • 22 Dec 2019 • Julian Nubert, Johannes Köhler, Vincent Berenz, Frank Allgöwer, Sebastian Trimpe
Fast feedback control and safety guarantees are essential in modern robotics.
1 code implementation • L4DC 2020 • Mona Buisson-Fenet, Friedrich Solowjow, Sebastian Trimpe
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way.
no code implementations • 29 Oct 2019 • Matteo Turchetta, Andreas Krause, Sebastian Trimpe
In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment.
1 code implementation • 7 Oct 2019 • Steve Heim, Alexander von Rohr, Sebastian Trimpe, Alexander Badri-Spröwitz
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.
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.
2 code implementations • 14 May 2019 • Andreas Doerr, Michael Volpp, Marc Toussaint, Sebastian Trimpe, Christian Daniel
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks.
no code implementations • 15 Dec 2018 • Matthias Neumann-Brosig, Alonso Marco, Dieter Schwarzmann, Sebastian Trimpe
Bayesian optimization is proposed for automatic learning of optimal controller parameters from experimental data.
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 • 10 Sep 2018 • Alexander von Rohr, Sebastian Trimpe, Alonso Marco, Peer Fischer, Stefano Palagi
Soft microrobots based on photoresponsive materials and controlled by light fields can generate a variety of different gaits.
no code implementations • 11 Jun 2018 • Michael Hertneck, Johannes Köhler, Sebastian Trimpe, Frank Allgöwer
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction.
no code implementations • 27 May 2018 • Arash Mehrjou, Friedrich Solowjow, Sebastian Trimpe, Bernhard Schölkopf
Apart from its application for encoding a sequence of observations, we propose to use the compression achieved by this encoding as a criterion for model selection.
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.
2 code implementations • ICML 2018 • Andreas Doerr, Christian Daniel, Martin Schiegg, Duy Nguyen-Tuong, Stefan Schaal, Marc Toussaint, Sebastian Trimpe
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
no code implementations • 20 Sep 2017 • Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.
no code implementations • 8 Mar 2017 • Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco, Stefan Schaal, Sebastian Trimpe
PID control architectures are widely used in industrial applications.
no code implementations • 3 Mar 2017 • Alonso Marco, Felix Berkenkamp, Philipp Hennig, Angela P. Schoellig, Andreas Krause, Stefan Schaal, Sebastian Trimpe
In practice, the parameters of control policies are often tuned manually.
no code implementations • 6 May 2016 • Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe
With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data.
1 code implementation • 19 Feb 2016 • Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes, Jeannette Bohg, Sebastian Trimpe, Stefan Schaal
To address this issue, we show how a recently published robustification method for Gaussian filters can be applied to the problem at hand.
no code implementations • 14 Sep 2015 • Manuel Wüthrich, Cristina Garcia Cifuentes, Sebastian Trimpe, Franziska Meier, Jeannette Bohg, Jan Issac, Stefan Schaal
The contribution of this paper is to show that any Gaussian filter can be made compatible with fat-tailed sensor models by applying one simple change: Instead of filtering with the physical measurement, we propose to filter with a pseudo measurement obtained by applying a feature function to the physical measurement.