Search Results for author: Sebastian Trimpe

Found 39 papers, 17 papers with code

Parameter Filter-based Event-triggered Learning

no code implementations5 Apr 2022 Sebastian Schlor, Friedrich Solowjow, Sebastian Trimpe

However, they usually come with a critical assumption - access to an accurate model of the system.

Active Learning

GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems

no code implementations24 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.

Safe Exploration

Local policy search with Bayesian optimization

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.

GoSafe: Globally Optimal Safe Robot Learning

1 code implementation27 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.

Safe Value Functions

no code implementations25 May 2021 Pierre-François Massiani, Steve Heim, Friedrich Solowjow, Sebastian Trimpe

Safety constraints and optimality are important, but sometimes conflicting criteria for controllers.

On exploration requirements for learning safety constraints

1 code implementation17 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.

Probabilistic robust linear quadratic regulators with Gaussian processes

1 code implementation17 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.

Gaussian Processes Robust Design

Learning-enhanced robust controller synthesis with rigorous statistical and control-theoretic guarantees

no code implementations7 May 2021 Christian Fiedler, Carsten W. Scherer, Sebastian Trimpe

The combination of machine learning with control offers many opportunities, in particular for robust control.

Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression

no code implementations6 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.

Gaussian Processes

Scaling Beyond Bandwidth Limitations: Wireless Control With Stability Guarantees Under Overload

no code implementations16 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.

Self-Driving Cars

Structure-preserving Gaussian Process Dynamics

no code implementations2 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.

Variational Inference

Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective

no code implementations11 Dec 2020 A. René Geist, Sebastian Trimpe

Accurate models of mechanical system dynamics are often critical for model-based control and reinforcement learning.

Gaussian Processes

Robot Learning with Crash Constraints

1 code implementation16 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.

Identifying Causal Structure in Dynamical Systems

1 code implementation6 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.

Causal Identification Causal Inference

A Kernel Two-sample Test for Dynamical Systems

no code implementations23 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.

Anomaly Detection Feature Engineering

Actively Learning Gaussian Process Dynamics

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.

Active Learning

Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

no code implementations29 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.

reinforcement-learning

A Learnable Safety Measure

1 code implementation7 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.

Gaussian Processes

Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties

no code implementations24 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.

Trajectory-Based Off-Policy Deep Reinforcement Learning

2 code implementations14 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.

Continuous Control Policy Gradient Methods +2

Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study

no code implementations15 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.

Deep Reinforcement Learning for Event-Triggered Control

1 code implementation13 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.

reinforcement-learning

Gait learning for soft microrobots controlled by light fields

no code implementations10 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.

Gaussian Processes

Learning an Approximate Model Predictive Controller with Guarantees

no code implementations11 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.

A Local Information Criterion for Dynamical Systems

no code implementations27 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.

Model Selection

Event-triggered Learning for Resource-efficient Networked Control

no code implementations5 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.

Probabilistic Recurrent State-Space Models

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.

Gaussian Processes Time Series +1

On the Design of LQR Kernels for Efficient Controller Learning

no code implementations20 Sep 2017 Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard.

Automatic LQR Tuning Based on Gaussian Process Global Optimization

no code implementations6 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.

Depth-Based Object Tracking Using a Robust Gaussian Filter

1 code implementation19 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.

Object Tracking Outlier Detection

Robust Gaussian Filtering using a Pseudo Measurement

no code implementations14 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.

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