17 citations · 50 across the 17 of their papers we have counts for
7 papers · 1 filter
Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination
Leonardo Barcellona, Andrii Zadaianchuk, Davide Allegro +3
A world model provides an agent with a representation of its environment, enabling it to predict the causal consequences of its actions. Current world models typically cannot direc…
MEMROC: Multi-Eye to Mobile RObot Calibration
Davide Allegro, Matteo Terreran, Stefano Ghidoni
This paper presents MEMROC (Multi-Eye to Mobile RObot Calibration), a novel motion-based calibration method that simplifies the process of accurately calibrating multiple cameras r…
WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
Alberto Bacchin, Leonardo Barcellona, Matteo Terreran +3
Robotic waste sorting poses significant challenges in both perception and manipulation, given the extreme variability of objects that should be recognized on a cluttered conveyor b…
SOOD-ImageNet: a Large-Scale Dataset for Semantic Out-Of-Distribution Image Classification and Semantic Segmentation
Alberto Bacchin, Davide Allegro, Stefano Ghidoni +1
Out-of-Distribution (OOD) detection in computer vision is a crucial research area, with related benchmarks playing a vital role in assessing the generalizability of models and thei…
Multi-view Pose Fusion for Occlusion-Aware 3D Human Pose Estimation
Laura Bragagnolo, Matteo Terreran, Davide Allegro +1
Robust 3D human pose estimation is crucial to ensure safe and effective human-robot collaboration. Accurate human perception,however, is particularly challenging in these scenarios…
Multi-Camera Hand-Eye Calibration for Human-Robot Collaboration in Industrial Robotic Workcells
Davide Allegro, Matteo Terreran, Stefano Ghidoni
In industrial scenarios, effective human-robot collaboration relies on multi-camera systems to robustly monitor human operators despite the occlusions that typically show up in a r…
Show and Grasp: Few-shot Semantic Segmentation for Robot Grasping through Zero-shot Foundation Models
Leonardo Barcellona, Alberto Bacchin, Matteo Terreran +2
The ability of a robot to pick an object, known as robot grasping, is crucial for several applications, such as assembly or sorting. In such tasks, selecting the right target to pi…