Alexandre Capone | DeepAI

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∙ 02/23/2023

Sharp Calibrated Gaussian Processes

While Gaussian processes are a mainstay for various engineering and scie...

0 Alexandre Capone, et al. ∙

∙ 06/28/2022

Dext-Gen: Dexterous Grasping in Sparse Reward Environments with Full Orientation Control

Reinforcement learning is a promising method for robotic grasping as it ...

0 Martin Schuck, et al. ∙

∙ 12/10/2021

Structure-Preserving Learning Using Gaussian Processes and Variational Integrators

Gaussian process regression is often applied for learning unknown system...

0 Jan Brüdigam, et al. ∙

∙ 09/06/2021

Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications

Gaussian processes have become a promising tool for various safety-criti...

0 Alexandre Capone, et al. ∙

∙ 05/25/2021

Gaussian Process-based Stochastic Model Predictive Control for Overtaking in Autonomous Racing

A fundamental aspect of racing is overtaking other race cars. Whereas pr...

0 Tim Brüdigam, et al. ∙

∙ 11/20/2020

The Value of Data in Learning-Based Control for Training Subset Selection

Despite the existence of formal guarantees for learning-based control ap...

0 Armin Lederer, et al. ∙

∙ 07/24/2020

Anticipating the Long-Term Effect of Online Learning in Control

Control schemes that learn using measurement data collected online are i...

0 Alexandre Capone, et al. ∙

∙ 05/25/2020

How Training Data Impacts Performance in Learning-based Control

When first principle models cannot be derived due to the complexity of t...

0 Armin Lederer, et al. ∙

∙ 05/04/2020

Localized active learning of Gaussian process state space models

The performance of learning-based control techniques crucially depends o...

0 Alexandre Capone, et al. ∙

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