67 citations · 138 across the 9 of their papers we have counts for
10 papers
A deep HST view of the open cluster NGC2158: binaries, mass functions, and M-dwarf discontinuity
A. V. Marchuk, F. Muratore, A. P. Milone +13
A significant fraction of stars in both the Galactic field and stellar clusters are members of binary systems. Understanding their properties is therefore essential for a comprehen…
Novel Pivoted Cholesky Decompositions for Efficient Gaussian Process Inference
Filip de Roos, Fabio Muratore
The Cholesky decomposition is a fundamental tool for solving linear systems with symmetric and positive definite matrices which are ubiquitous in linear algebra, optimization, and…
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Pseudo-Likelihood Inference
Theo Gruner, Boris Belousov, Fabio Muratore +2
Simulation-Based Inference (SBI) is a common name for an emerging family of approaches that infer the model parameters when the likelihood is intractable. Existing SBI methods eith…
Distilled Domain Randomization
Julien Brosseit, Benedikt Hahner, Fabio Muratore +2
Deep reinforcement learning is an effective tool to learn robot control policies from scratch. However, these methods are notorious for the enormous amount of required training dat…
Robot Learning from Randomized Simulations: A Review
Fabio Muratore, Fabio Ramos, Greg Turk +3
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to gen…
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients
João Carvalho, Davide Tateo, Fabio Muratore +1
Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate…
Underactuated Waypoint Trajectory Optimization for Light Painting Photography
Christian Eilers, Jonas Eschmann, Robin Menzenbach +3
Despite their abundance in robotics and nature, underactuated systems remain a challenge for control engineering. Trajectory optimization provides a generally applicable solution,…
Data-efficient Domain Randomization with Bayesian Optimization
Fabio Muratore, Christian Eilers, Michael Gienger +1
When learning policies for robot control, the required real-world data is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunate…
Assessing Transferability from Simulation to Reality for Reinforcement Learning
Fabio Muratore, Michael Gienger, Jan Peters
Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviat…