Raffaello Camoriano — accepted AI-conference papers

2025

Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering

Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti, Diego Ferigo, Giulio Romualdi, Giuseppe L'Erario +2

IROS 2025

Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data. While these approaches make more realistic motions possible, they are limited by the amount of available motion data, and do not

Cited by 0 Source ↗Scholar ↗

2024

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers

Eros Fanì, Raffaello Camoriano, Barbara Caputo, Marco Ciccone

ICML 2024poster

Federated Learning (FL) methods often struggle in highly statistically heterogeneous settings. Indeed, non-IID data distributions cause client drift and biased local solutions, particularly pronounced in the final classification layer, negatively impacting convergence speed and accuracy. To address…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗

2024

Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning

Dario Di Domenico, Nicoló Boccardo, Andrea Marinelli, Michele Canepa, Emanuele Gruppioni, Matteo Laffranchi +1

RA-L 2024

Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized contro

Cited by 13 Source ↗code ↗Scholar ↗

2023

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

Gabriele Tiboni, Raffaello Camoriano, Tatiana Tommasi

IROS 2023poster

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. Yet, existing solutions make strong assumptions on the form of input surfaces and the nature of output paths, resulti…

Cited by 6 Source ↗Scholar ↗

2022

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

Paolo Maria Viceconte, Raffaello Camoriano, Giulio Romualdi, Diego Ferigo, Stefano Dafarra, Silvio Traversaro +3

RA-L 2022

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human-like</i> trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for characte

Cited by 22 Source ↗Scholar ↗

2021

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco +1

RA-L 2021

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires de

Cited by 22 Source ↗Scholar ↗

2021

Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models

Gian Maria Marconi, Raffaello Camoriano, Lorenzo Rosasco, Carlo Ciliberto

RA-L 2021

With the recent advances in machine learning, problems that traditionally would require accurate modeling to be solved analytically can now be successfully approached with data-driven strategies. Among these, computing the inverse kinematics of a redundant robot arm poses a significant challenge due

Cited by 4 Source ↗code ↗Scholar ↗

2019

Learning to Sequence Multiple Tasks with Competing Constraints

Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco +1

IROS 2019poster

Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem o…

Cited by 8 Source ↗Scholar ↗

2018

Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification

Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone

NeurIPS 2018poster

This paper studies the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled approach, but the corresponding computational burden is hardly sustainable in large-scale problems and devising efficient alte…

Cited by 109 Source ↗PDF ↗code ↗Scholar ↗

2017

Incremental robot learning of new objects with fixed update time

Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto, Lorenzo Natale, Lorenzo Rosasco, Giorgio Metta

ICRA 2017poster

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. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC)…

Cited by 51 Source ↗code ↗Scholar ↗

2016

Generalization Properties and Implicit Regularization for Multiple Passes SGM

Junhong Lin, Raffaello Camoriano, Lorenzo Rosasco

ICML 2016poster

We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning ei…

Cited by 85 Source ↗PDF ↗Scholar ↗

2016

Incremental semiparametric inverse dynamics learning

Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco, Giorgio Metta, Francesco Nori

ICRA 2016

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior informatio

Cited by 51 Source ↗Scholar ↗

2016

NYTRO: When Subsampling Meets Early Stopping

Raffaello Camoriano, Tomás Angles, Alessandro Rudi, Lorenzo Rosasco

AISTATS 2016poster

Early stopping is a well known approach to reduce the time complexity for performing training and model selection of large scale learning machines. On the other hand, memory/space (rather than time) complexity is the main constraint in many applications, and randomized subsampling techniques have b…

Cited by 43 Source ↗PDF ↗code ↗Scholar ↗

2015

Less is More: Nyström Computational Regularization

Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco

NeurIPS 2015oral

We study Nyström 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. In particular, we prove that these approaches can achieve optimal learning bound…

Cited by 374 Source ↗PDF ↗Scholar ↗