5 papers
R2F: Repurposing Ray Frontiers for LLM-free Object Navigation
Francesco Argenziano, John Mark Alexis Marcelo, Michele Brienza +5
Zero-shot open-vocabulary object navigation has progressed rapidly with the emergence of large Vision-Language Models (VLMs) and Large Language Models (LLMs), now widely used as hi…
Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning
Emanuele Musumeci, Michele Brienza, Francesco Argenziano +4
Embodied agents need to plan and act reliably in real and complex 3D environments. Classical planning (e.g., PDDL) offers structure and guarantees, but in practice it fails under n…
LOST-3DSG: Lightweight Open-Vocabulary 3D Scene Graphs with Semantic Tracking in Dynamic Environments
Sara Micol Ferraina, Michele Brienza, Francesco Argenziano +4
Tracking objects that move within dynamic environments is a core challenge in robotics. Recent research has advanced this topic significantly; however, many existing approaches rem…
LLCoach: Generating Robot Soccer Plans using Multi-Role Large Language Models
Michele Brienza, Emanuele Musumeci, Vincenzo Suriani +4
The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup o…
Play Everywhere: A Temporal Logic based Game Environment Independent Approach for Playing Soccer with Robots
Vincenzo Suriani, Emanuele Musumeci, Daniele Nardi +1
Robots playing soccer often rely on hard-coded behaviors that struggle to generalize when the game environment change. In this paper, we propose a temporal logic based approach tha…