19 citations · 31 across the 7 of their papers we have counts for
9 papers
Observe, Share, or Predict? Disentangling Partner Information in Embodied Multi-Agent Collaboration
Adrián Szlatincsán, Filippo Ziliotto, Bruno Lepri +3
LLMs are used as high-level controllers for embodied agents, but it remains unclear whether they support collaborative reasoning, such as anticipating a collaborator's actions in r…
Mirror, Mirror on the Wall: Can VLM Agents Tell Who They Are at All?
Filippo Ziliotto, Ciro Beneduce, Bruno Lepri +3
In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogous functional capability emer…
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
Alessandro Daniele, Tommaso Campari, Sagar Malhotra +1
Deep Learning (DL) techniques have achieved remarkable successes in recent years. However, their ability to generalize and execute reasoning tasks remains a challenge. A potential…
MOPA: Modular Object Navigation with PointGoal Agents
Sonia Raychaudhuri, Tommaso Campari, Unnat Jain +2
We propose a simple but effective modular approach MOPA (Modular ObjectNav with PointGoal agents) to systematically investigate the inherent modularity of the object navigation tas…
Exploiting Proximity-Aware Tasks for Embodied Social Navigation
Enrico Cancelli, Tommaso Campari, Luciano Serafini +2
Learning how to navigate among humans in an occluded and spatially constrained indoor environment, is a key ability required to embodied agent to be integrated into our society. In…
Retrospectives on the Embodied AI Workshop
Matt Deitke, Dhruv Batra, Yonatan Bisk +36
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
Alessandro Daniele, Tommaso Campari, Sagar Malhotra +1
Neuro-Symbolic (NeSy) integration combines symbolic reasoning with Neural Networks (NNs) for tasks requiring perception and reasoning. Most NeSy systems rely on continuous relaxati…
Online Learning of Reusable Abstract Models for Object Goal Navigation
Tommaso Campari, Leonardo Lamanna, Paolo Traverso +2
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 Ob…
Exploiting Scene-specific Features for Object Goal Navigation
Tommaso Campari, Paolo Eccher, Luciano Serafini +1
Can the intrinsic relation between an object and the room in which it is usually located help agents in the Visual Navigation Task? We study this question in the context of Object…