25 citations · 33 across the 15 of their papers we have counts for
9 papers · 1 filter
Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control
J. M. A. Marcelo, M. Brienza, E. Bugli +4
Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies. However, most robotic performances rem…
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…
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…
Multi Robot Coordination in Highly Dynamic Environments: Tackling Asymmetric Obstacles and Limited Communication
Vincenzo Suriani, Daniele Affinita, Domenico D. Bloisi +1
Coordinating a fully distributed multi-agent system (MAS) can be challenging when the communication channel has very limited capabilities in terms of sending rate and packet payloa…
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…
EMPOWER: Embodied Multi-role Open-vocabulary Planning with Online Grounding and Execution
Francesco Argenziano, Michele Brienza, Vincenzo Suriani +2
Task planning for robots in real-life settings presents significant challenges. These challenges stem from three primary issues: the difficulty in identifying grounded sequences of…
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…
Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication
Daniele Affinita, Flavio Volpi, Valerio Spagnoli +3
RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soc…