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Stefano Ghidoni

8 accepted papers

2025

Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination

ICLR 2025poster

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 directly and explicitly imitate the actual environment in front of a robot, often resulting in unrealistic behaviors and hall…

2024

Multi-Camera Hand-Eye Calibration for Human-Robot Collaboration in Industrial Robotic Workcells

RA-L 2024

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 robotic workcell. In this scenario, precise localization of the person in the robot coordinate system is essential, makin

Cited by 8SourcecodeScholar
2024

WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks

IROS 2024poster

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 belt. While deep learning has proven effective in solving complex tasks, the necessity for extensive data collection and…

Cited by 3SourcecodeScholar
2023

FSG-Net: a Deep Learning model for Semantic Robot Grasping through Few-Shot Learning

ICRA 2023poster

Robot grasping has been widely studied in the last decade. Recently, Deep Learning made possible to achieve remarkable results in grasp pose estimation, using depth and RGB images. However, only few works consider the choice of the object to grasp. Moreover, they require a huge amount of data for ge…

Cited by 7SourceScholar
2020

A Control Framework Definition to Overcome Position/Interaction Dynamics Uncertainties in Force-Controlled Tasks

ICRA 2020poster

Within the Industry 4.0 context, industrial robots need to show increasing autonomy. The manipulator has to be able to react to uncertainties/changes in the working environment, displaying a robust behavior. In this paper, a control framework is proposed to perform industrial interaction tasks in un…

Cited by 20SourceScholar
2018

Multi-View 3D Entangled Forest for Semantic Segmentation and Mapping

ICRA 2018poster

Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In thi…

Cited by 17SourceScholar