3 papers
cs.CV2025
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…
cs.RO2025
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…
cs.AI2024
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…