183 citations · 1.5k across the 113 of their papers we have counts for
122 papers
Q-learning with temporal memory to navigate turbulence
Marco Rando, Martin James, Alessandro Verri +2
We consider the problem of olfactory searches in a turbulent environment. We focus on agents that respond solely to odor stimuli, with no access to spatial perception nor prior inf…
RESPRECT: Speeding-up Multi-fingered Grasping with Residual Reinforcement Learning
Federico Ceola, Lorenzo Rosasco, Lorenzo Natale
Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dextero…
On Learning the Optimal Regularization Parameter in Inverse Problems
Jonathan Chirinos Rodriguez, Ernesto De Vito, Cesare Molinari +2
Selecting the best regularization parameter in inverse problems is a classical and yet challenging problem. Recently, data-driven approaches have become popular to tackle this chal…
Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling
Antoine Chatalic, Nicolas Schreuder, Ernesto De Vito +1
In this work we consider the problem of numerical integration, i.e., approximating integrals with respect to a target probability measure using only pointwise evaluations of the in…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
A Grasp Pose is All You Need: Learning Multi-fingered Grasping with Deep Reinforcement Learning from Vision and Touch
Federico Ceola, Elisa Maiettini, Lorenzo Rosasco +1
Multi-fingered robotic hands have potential to enable robots to perform sophisticated manipulation tasks. However, teaching a robot to grasp objects with an anthropomorphic hand is…