Search Results for author: Raffaello Camoriano

Found 15 papers, 7 papers with code

TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning

1 code implementation20 Apr 2023 Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Raffaello Camoriano, Bedřich Rus

Furthermore, DRL aims to find an optimal control policy rather than a static parameter configuration, particularly suitable for dynamic processes involving sequential decision-making.

Decision Making reinforcement-learning

Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation

no code implementations10 Feb 2023 Andrea Maracani, Raffaello Camoriano, Elisa Maiettini, Davide Talon, Lorenzo Rosasco, Lorenzo Natale

Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks.

Unsupervised Domain Adaptation

PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting

no code implementations13 Nov 2022 Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi

Popular industrial robotic problems such as spray painting and welding require (i) conditioning on free-shape 3D objects and (ii) planning of multiple trajectories to solve the task.

Structured Prediction for CRiSP Inverse Kinematics Learning with Misspecified Robot Models

1 code implementation25 Feb 2021 Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto

Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinematics map.

Structured Prediction

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

no code implementations28 Dec 2020 Elisa Maiettini, Andrea Maracani, Raffaello Camoriano, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale

We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning).

Active Learning Line Detection +4

Large-scale Kernel Methods and Applications to Lifelong Robot Learning

no code implementations11 Dec 2019 Raffaello Camoriano

In this thesis, we focus on kernel methods, a theoretically sound and effective class of learning algorithms yielding nonparametric estimators.

Generalization Properties and Implicit Regularization for Multiple Passes SGM

1 code implementation26 May 2016 Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions.

Incremental Robot Learning of New Objects with Fixed Update Time

1 code implementation17 May 2016 Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta

We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment.

Active Learning General Classification +2

Online semi-parametric learning for inverse dynamics modeling

no code implementations17 Mar 2016 Diego Romeres, Mattia Zorzi, Raffaello Camoriano, Alessandro Chiuso

This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model.

Incremental Semiparametric Inverse Dynamics Learning

no code implementations18 Jan 2016 Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori

This paper presents a novel approach for incremental semiparametric inverse dynamics learning.

NYTRO: When Subsampling Meets Early Stopping

1 code implementation19 Oct 2015 Tomas Angles, Raffaello Camoriano, Alessandro Rudi, Lorenzo Rosasco

Early stopping is a well known approach to reduce the time complexity for performing training and model selection of large scale learning machines.

Model Selection regression

Less is More: Nyström Computational Regularization

1 code implementation NeurIPS 2015 Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco

We study Nystr\"om type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered.

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