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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…
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…
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…