45 citations · 176 across the 17 of their papers we have counts for
19 papers
SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning
Evelyn D'Elia, Weishu Zhan, Giulio Turrisi +5
Reinforcement learning (RL) algorithms classically suffer from poor sample efficiency. In robotics, a recent line of work has emerged addressing this problem by encoding physics pr…
Directional Constraints for Efficient Exploration in Safe Reinforcement Learning
Paolo Magliano, Puze Liu, Jan Peters +2
Reinforcement Learning has revolutionized the landscape of robotic research, allowing robust learning of complex robotic skills in simulation. However, real-world deployment in ope…
Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti +5
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 the…
Key Design Choices in Source-Free Unsupervised Domain Adaptation: An In-depth Empirical Analysis
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical unde…
Learning Rhythmic Trajectories with Geometric Constraints for Laser-Based Skincare Procedures
Anqing Duan, Wanli Liuchen, Jinsong Wu +3
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This paper investigates the automation challenge…
A Structured Prediction Approach for Robot Imitation Learning
Anqing Duan, Iason Batzianoulis, Raffaello Camoriano +3
We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observe…
TempoRL: laser pulse temporal shape optimization with Deep Reinforcement Learning
Francesco Capuano, Davorin Peceli, Gabriele Tiboni +2
High Power Laser's (HPL) optimal performance is essential for the success of a wide variety of experimental tasks related to light-matter interactions. Traditionally, HPL parameter…
Key Design Choices for Double-Transfer in Source-Free Unsupervised Domain Adaptation
Andrea Maracani, Raffaello Camoriano, Elisa Maiettini +3
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks. However, target domain labels are not acces…
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
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 tas…
On the Emergence of Whole-body Strategies from Humanoid Robot Push-recovery Learning
Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte +4
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 si…
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach
Elisa Maiettini, Andrea Maracani, Raffaello Camoriano +4
Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodimen…
Large-scale Kernel Methods and Applications to Lifelong Robot Learning
Raffaello Camoriano
As the size and richness of available datasets grow larger, the opportunities for solving increasingly challenging problems with algorithms learning directly from data grow at the…
Derivative-free online learning of inverse dynamics models
Diego Romeres, Mattia Zorzi, Raffaello Camoriano +2
This paper discusses online algorithms for inverse dynamics modelling in robotics. Several model classes including rigid body dynamics (RBD) models, data-driven models and semipara…
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi +2
In this paper, we study the problem of deriving fast and accurate classification algorithms with uncertainty quantification. Gaussian process classification provides a principled a…
Generalization Properties and Implicit Regularization for Multiple Passes SGM
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. We show that, in the absence of…
Incremental Robot Learning of New Objects with Fixed Update Time
Raffaello Camoriano, Giulia Pasquale, Carlo Ciliberto +3
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 experi…
Incremental Semiparametric Inverse Dynamics Learning
Raffaello Camoriano, Silvio Traversaro, Lorenzo Rosasco +2
This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based o…
NYTRO: When Subsampling Meets Early Stopping
Tomas Angles, Raffaello Camoriano, Alessandro Rudi +1
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…
Less is More: Nyström Computational Regularization
Alessandro Rudi, Raffaello Camoriano, Lorenzo Rosasco
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 probabilit…