4 citations · 8 across the 6 of their papers we have counts for
7 papers
From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models
Christian Gumbsch, Leonardo Barcellona, Lennard Schünemann +7
Reinforcement learning relies on accurate reward functions, which are often hand-crafted or even unavailable in real-world applications, such as robotics. Recent work has explored…
Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations
Andrii Zadaianchuk, Leonardo Barcellona, Lennard Schuenemann +7
Accurately reconstructing complex full multi-object scenes from sparse observations remains a core challenge in computer vision and a key step toward scalable and reliable simulati…
SkelSplat: Robust Multi-view 3D Human Pose Estimation with Differentiable Gaussian Rendering
Laura Bragagnolo, Leonardo Barcellona, Stefano Ghidoni
Accurate 3D human pose estimation is fundamental for applications such as augmented reality and human-robot interaction. State-of-the-art multi-view methods learn to fuse predictio…
Leveraging Multi-View Weak Supervision for Occlusion-Aware Multi-Human Parsing
Laura Bragagnolo, Matteo Terreran, Leonardo Barcellona +1
Multi-human parsing is the task of segmenting human body parts while associating each part to the person it belongs to, combining instance-level and part-level information for fine…
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…