114 citations · 321 across the 8 of their papers we have counts for
11 papers
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization
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. In real-world a…
Trajectory-Based Off-Policy Deep Reinforcement Learning
Andreas Doerr, Michael Volpp, Marc Toussaint +2
Policy gradient methods are powerful reinforcement learning algorithms and have been demonstrated to solve many complex tasks. However, these methods are also data-inefficient, aff…
Probabilistic Recurrent State-Space Models
Andreas Doerr, Christian Daniel, Martin Schiegg +4
State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification. Deterministic versions of SSMs (e.g. LSTMs) p…
On the Design of LQR Kernels for Efficient Controller Learning
Alonso Marco, Philipp Hennig, Stefan Schaal +1
Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct cont…
Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
Andreas Doerr, Duy Nguyen-Tuong, Alonso Marco +2
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in pr…
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…
Predictive and Self Triggering for Event-based State Estimation
Sebastian Trimpe
Event-based state estimation can achieve estimation quality comparable to traditional time-triggered methods, but with a significantly lower number of samples. In networked estimat…
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…
Depth-Based Object Tracking Using a Robust Gaussian Filter
Jan Issac, Manuel Wüthrich, Cristina Garcia Cifuentes +3
We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard Gaussian filter to this pro…
Robust Gaussian Filtering using a Pseudo Measurement
Manuel Wüthrich, Cristina Garcia Cifuentes, Sebastian Trimpe +4
Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor…
A New Perspective and Extension of the Gaussian Filter
Manuel Wüthrich, Sebastian Trimpe, Daniel Kappler +1
The Gaussian Filter (GF) is one of the most widely used filtering algorithms; instances are the Extended Kalman Filter, the Unscented Kalman Filter and the Divided Difference Filte…