Search Results for author: Alonso Marco

Found 11 papers, 3 papers with code

Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models

no code implementations13 Sep 2023 Alonso Marco, Elias Morley, Claire J. Tomlin

In this paper, we propose (i) a novel approach to embed existing domain knowledge in the kernel and (ii) an OoD online runtime monitor, based on receding-horizon predictions.

Navigate Out-of-Distribution Detection

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.

Bayesian Optimization

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.

Bayesian Optimization

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.

Bayesian Optimization regression

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.

Bayesian Optimization Gaussian Processes

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.

Bayesian Optimization

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