15 papers
GORDON: Graph-based Object-centric Rewards for Decomposition of Long-Horizon Manipulation
Andrea Protopapa, Davide Buoso, Francesca Pistilli +2
Learning long-horizon manipulation skills with reinforcement learning remains challenging due to the complexity of reward design, the limited guidance of sparse rewards, and the hi…
Learning to Evolve Scenes: Reasoning about Human Activities with Scene Graphs
Francesca Pistilli, Simone Alberto Peirone, Giuseppe Averta
Understanding human behavior while interacting with the surrounding world is crucial for many applications of embodied AI. First-person videos are particularly informative for this…
HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding
Andrea Zenotto, Simone Alberto Peirone, Francesca Pistilli +1
Procedural activities follow well-defined structures: whether we consider a cooking recipe or a mechanic repairing a car, these activities naturally decompose in a hierarchy of ste…
RGB-only Active 3D Scene Graph Generation for Indoor Mobile Robots
Giorgia Modi, Davide Buoso, Giuseppe Averta +1
Current approaches to 3D scene graph generation rely on dedicated depth sensors, such as LiDAR or RGB-D cameras, for metric 3D reconstruction. This limits deployment to specialized…
Fixed External Cameras as Common Prior Maps for Active 3D Scene Graph Generation
Giorgia Modi, Davide Buoso, Giuseppe Averta +1
Commonly available prior information, such as BIM models, floor plans, and remote sensing images, can provide valuable geometric and semantic context for autonomous robotic systems…
GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
Davide Buoso, Andrea Protopapa, Stefano Di Carlo +2
Learning visuomotor policies from scarce expert demonstrations remains a core challenge in robotic manipulation. A primary hurdle lies in distilling high-dimensional RGB representa…