Search Results for author: Alonso Marco

Found 10 papers, 3 papers with code

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.

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.

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.

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.

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

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.

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