5 papers · 1 filter
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…
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…
Select2Plan: Training-Free ICL-Based Planning through VQA and Memory Retrieval
Davide Buoso, Luke Robinson, Giuseppe Averta +3
This study explores the potential of off-the-shelf Vision-Language Models (VLMs) for high-level robot planning in the context of autonomous navigation. Indeed, while most of existi…