3 papers
cs.LG2026
Discrete World Models via Regularization
Davide Bizzaro, Luciano Serafini
World models aim to capture the states and dynamics of an environment in a compact latent space. Moreover, using Boolean state representations is particularly useful for search heu…
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.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…