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Tommaso Campari

6 accepted papers

2026

PersONAL: Towards a Comprehensive Benchmark for Personalized Embodied Agents

Filippo Ziliotto, Jelin Raphael Akkara, Alessandro Daniele, Lamberto Ballan, Luciano Serafini, Tommaso Campari
ICRA 2026poster

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-centered scenarios, such as domestic households, remains challenging, particularly due to the difficulty of modeling individua…

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2025

Following the Human Thread in Social Navigation

Luca Scofano, Alessio Sampieri, Tommaso Campari, Valentino Sacco, Indro Spinelli, Lamberto Ballan +1
ICLR 2025spotlight

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 agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human traje…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2025

TANGO: Training-free Embodied AI Agents for Open-world Tasks

Filippo Ziliotto, Tommaso Campari, Luciano Serafini, Lamberto Ballan
CVPR 2025poster

Large Language Models (LLMs) have demonstrated excellent capabilities in composing various modules together to create programs that can perform complex reasoning tasks on images. In this paper, we propose TANGO, an approach that extends the program composition via LLMs already observed for images, a…

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2023

Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions

Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini
IJCAI 2023poster

Neuro-Symbolic (NeSy) integration combines symbolic reasoning with Neural Networks (NNs) for tasks requiring perception and reasoning. Most NeSy systems rely on continuous relaxation of logical knowledge, and no discrete decisions are made within the model pipeline. Furthermore, these methods assume…

Cited by 24Source ↗PDF ↗Scholar ↗
2023

Exploiting Proximity-Aware Tasks for Embodied Social Navigation

Enrico Cancelli, Tommaso Campari, Luciano Serafini, Angel X. Chang, Lamberto Ballan
ICCV 2023poster

Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agents to be integrated into our society. In this paper, we propose an end-to-end architecture that exploits Proximity-Aware Tasks (referred as to Risk and Proximi…

Cited by 15PDF ↗code ↗Scholar ↗
2022

Online Learning of Reusable Abstract Models for Object Goal Navigation

Tommaso Campari, Leonardo Lamanna, Paolo Traverso, Luciano Serafini, Lamberto Ballan
CVPR 2022poster

In this paper, we present a novel approach to incrementally learn an Abstract Model of an unknown environment, and show how an agent can reuse the learned model for tackling the Object Goal Navigation task. The Abstract Model is a finite state machine in which each state is an abstraction of a state…

Cited by 26PDF ↗Scholar ↗