22 citations · 49 across the 12 of their papers we have counts for
12 papers
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…
iCub Detecting Gazed Objects: A Pipeline Estimating Human Attention
Shiva Hanifi, Elisa Maiettini, Maria Lombardi +1
This research report explores the role of eye gaze in human-robot interactions and proposes a learning system for detecting objects gazed at by humans using solely visual feedback.…
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…
Self-improving object detection via disagreement reconciliation
Gianluca Scarpellini, Stefano Rosa, Pietro Morerio +2
Object detectors often experience a drop in performance when new environmental conditions are insufficiently represented in the training data. This paper studies how to automatical…
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…
Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors
Gabriele M. Caddeo, Nicola A. Piga, Fabrizio Bottarel +1
In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial config…
Towards Confidence-guided Shape Completion for Robotic Applications
Andrea Rosasco, Stefano Berti, Fabrizio Bottarel +2
Many robotic tasks involving some form of 3D visual perception greatly benefit from a complete knowledge of the working environment. However, robots often have to tackle unstructur…
One-Shot Open-Set Skeleton-Based Action Recognition
Stefano Berti, Andrea Rosasco, Michele Colledanchise +1
Action recognition is a fundamental capability for humanoid robots to interact and cooperate with humans. This application requires the action recognition system to be designed so…
iCub Knows Where You Look: Exploiting Social Cues for Interactive Object Detection Learning
Maria Lombardi, Elisa Maiettini, Vadim Tikhanoff +1
Performing joint interaction requires constant mutual monitoring of own actions and their effects on the other's behaviour. Such an action-effect monitoring is boosted by social cu…
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
Federico Ceola, Elisa Maiettini, Giulia Pasquale +3
The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast…
Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis
Federico Vasile, Elisa Maiettini, Giulia Pasquale +3
We consider the task of object grasping with a prosthetic hand capable of multiple grasp types. In this setting, communicating the intended grasp type often requires a high user co…
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…