6 papers
IMPRINT: Image-Conditioned Query Enrichment for Long-Tail Object Goal Navigation
Jelin Raphael Akkara, Filippo Ziliotto, Luciano Serafini +2
Embodied AI increasingly relies on queryable semantic maps built from pre-trained vision-language models to enable zero-shot Object Goal Navigation (ObjectNav). However, existing a…
What VGGT Knows About Overlap: Probing Geometric Foundation Models for Co-Visibility
Filippo Ziliotto, Luciano Serafini, Lamberto Ballan +1
A fundamental challenge in 3D reconstruction and robotic localization is co-visibility: determining which image pairs share overlapping visible surfaces, particularly in scenarios…
MLFM: Multi-Layered Feature Maps for Richer Language Understanding in Zero-Shot Semantic Navigation
Sonia Raychaudhuri, Enrico Cancelli, Tommaso Campari +3
Recent progress in large vision-language models has driven improvements in language-based semantic navigation, where an embodied agent must reach a target object described in natur…
PersONAL: Towards a Comprehensive Benchmark for Personalized Embodied Agents
Filippo Ziliotto, Jelin Raphael Akkara, Alessandro Daniele +3
Recent advances in Embodied AI have enabled agents to perform increasingly complex tasks and adapt to diverse environments. However, deploying such agents in realistic human-center…
Following the Human Thread in Social Navigation
Luca Scofano, Alessio Sampieri, Tommaso Campari +4
The success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the ag…
TANGO: Training-free Embodied AI Agents for Open-world Tasks
Filippo Ziliotto, Tommaso Campari, Luciano Serafini +1
Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. I…