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Statistical heterogeneity in Federated Learning (FL) often leads to client drift and biased local solutions. Prior work in the literature shows that client drift particularly affects the parameters of the classification layer, hindering both convergence and accuracy. While Personalized FL (PFL) addresses this by allowing client-specific models, it can overlook valuable global knowledge. This paper...Show More
Learning Rhythmic Trajectories With Geometric Constraints for Laser-Based Skincare Procedures
Anqing Duan;Wanli Liuchen;Jinsong Wu;Raffaello Camoriano;Lorenzo Rosasco;David Navarro-Alarcon
The increasing deployment of robots has significantly enhanced the automation levels across a wide and diverse range of industries. This article investigates the automation challenges of laser-based dermatology procedures in the beauty industry. This group of related manipulation tasks involves delivering energy from a cosmetic laser onto the skin with repetitive patterns. To automate this procedu...Show More
Long-Term Upper-Limb Prosthesis Myocontrol via High-Density sEMG and Incremental Learning
Dario Di Domenico;Nicolò Boccardo;Andrea Marinelli;Michele Canepa;Emanuele Gruppioni;Matteo Laffranchi;Raffaello Camoriano
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 controllers from user data. While promising, they often suffer from distribution shift during long-term us...Show More
PaintNet: Unstructured Multi-Path Learning from 3D Point Clouds for Robotic Spray Painting
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
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, resulting in limited approaches unable to cope with real-data variability. By leveraging on recent advances...Show More
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach
Elisa Maiettini;Andrea Maracani;Raffaello Camoriano;Giulia Pasquale;Vadim Tikhanoff;Lorenzo Rosasco;Lorenzo Natale
2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)
Year: 2022 | Conference Paper |
Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing the object of interest, hand-held. However, learning solely from this data may ...Show More
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;Giuseppe Oriolo;Lorenzo Rosasco;Daniele Pucci
Human-like trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for character animation based on training human-like models directly on motion capture data. Such methods proved effective in virtual environments, mainly focusing on trajectory visualization. This letter presents ...Show More
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;Daniele Pucci
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 demanding tuning of parameters and switching logic between specifically-designed controllers for handl...Show More
Structured Prediction for CRiSP Inverse Kinematics Learning With Misspecified Robot Models
Gian Maria Marconi;Raffaello Camoriano;Lorenzo Rosasco;Carlo Ciliberto
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 to the non-linear structure of the robot, the hard joint constraints and the non-invertible kinemat...Show More
Derivative-Free Online Learning of Inverse Dynamics Models
Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Silvio Traversaro;Alessandro Chiuso
This paper discusses online algorithms for inverse dynamics modeling in robotics. Several model classes, including rigid body dynamics models, data-driven models and semiparametric models (which are combination of the previous two classes), are placed in a common framework. While model classes used in the literature typically exploit joint velocities and accelerations, which needs to be approximat...Show More
Learning to Sequence Multiple Tasks with Competing Constraints
Anqing Duan;Raffaello Camoriano;Diego Ferigo;Yanlong Huang;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019 | Conference Paper |
Cited by: Papers (7)
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 of multi-tasks sequencing. Conceivably, sequencing different atomic tasks can further augment the rob...Show More
Constrained DMPs for Feasible Skill Learning on Humanoid Robots
Anqing Duan;Raffaello Camoriano;Diego Ferigo;Daniele Calandriello;Lorenzo Rosasco;Daniele Pucci
2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)
Year: 2018 | Conference Paper |
Cited by: Papers (15)
In the context of humanoid skill learning, movement primitives have gained much attention because of their compact representation and convenient combination with a myriad of optimization approaches. Among them, a well-known scheme is to use Dynamic Movement Primitives (DMPs) with reinforcement learning (RL) algorithms. While various remarkable results have been reported, skill learning with physic...Show More
Incremental robot learning of new objects with fixed update time
Raffaello Camoriano;Giulia Pasquale;Carlo Ciliberto;Lorenzo Natale;Lorenzo Rosasco;Giorgio Metta
2017 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2017 | Conference Paper |
Cited by: Papers (23)
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) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presen...Show More
Online semi-parametric learning for inverse dynamics modeling
Diego Romeres;Mattia Zorzi;Raffaello Camoriano;Alessandro Chiuso
2016 IEEE 55th Conference on Decision and Control (CDC)
Year: 2016 | Conference Paper |
Cited by: Papers (30)
This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robo...Show More
Incremental semiparametric inverse dynamics learning
Raffaello Camoriano;Silvio Traversaro;Lorenzo Rosasco;Giorgio Metta;Francesco Nori
2016 IEEE International Conference on Robotics and Automation (ICRA)
Year: 2016 | Conference Paper |
Cited by: Papers (32)
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 information on the mechanical properties of the system. The result is an incremental semiparametric approach, ...Show More
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