9 citations · 13 across the 10 of their papers we have counts for
6 papers · 1 filter
Calib3R: Hand-Eye Calibration and 3D Metric-Scaled Scene Reconstruction with 3D Foundation Models
Davide Allegro, Matteo Terreran, Stefano Ghidoni
Robots often rely on RGB images for tasks like manipulation. However, reliable interaction typically requires a 3D scene representation that is metric-scaled and aligned with the r…
Data efficient Robotic Object Throwing with Model-Based Reinforcement Learning
Niccolò Turcato, Giulio Giacomuzzo, Matteo Terreran +3
Pick-and-place (PnP) operations, featuring object grasping and trajectory planning, are fundamental in industrial robotics applications. Despite many advancements in the field, PnP…
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…
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…
A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups
Daniele Evangelista, Emilio Olivastri, Davide Allegro +2
Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of o…