25 citations · 33 across the 21 of their papers we have counts for
22 papers
iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary Domains
Vincenzo Guarino, Emanuele Musumeci, Vincenzo Suriani +1
Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks. For example, embodied age…
An LLM-Based Automatic Sportscast Solution for Robot Soccer Matches
Francesco Petri, Michele Brienza, Daniele Nardi +3
RoboCup has always been a scenario to develop systems that solve real-world problems. Driven by the main goal of playing against the 2050 FIFA World Cup champions, the RoboCup Socc…
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…
Vision-Based Dribbling for Humanoid Soccer via Privileged Representation Learning
Flavio Maiorana, Valerio Spagnoli, Eugenio Bugli +5
Recent advances in humanoid robotics have highlighted the importance of deployable loco-manipulation skills. Dribbling a soccer ball while evading active opponents requires simulta…
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…
Guarding the Guardrails: A Taxonomy-Driven Approach to Jailbreak Detection
Francesco Giarrusso, Olga E. Sorokoletova, Vincenzo Suriani +1
Jailbreaking techniques pose a significant threat to the safety of Large Language Models (LLMs). Existing defenses typically focus on single-turn attacks, lack coverage across lang…
Dynamic Objects Relocalization in Changing Environments with Flow Matching
Francesco Argenziano, Miguel Saavedra-Ruiz, Sacha Morin +2
Task and motion planning are long-standing challenges in robotics, especially when robots have to deal with dynamic environments exhibiting long-term dynamics, such as households o…
Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning
Abdel Hakim Drid, Vincenzo Suriani, Daniele Nardi +1
Navigating and understanding complex and unknown environments autonomously demands more than just basic perception and movement from embodied agents. Truly effective exploration re…
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…
Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots
Can Lin, Daniele Affinita, Marco E. P. Zimmatore +3
Robust and accurate ball detection is a critical component for autonomous humanoid soccer robots, particularly in dynamic and challenging environments such as RoboCup outdoor field…
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…
Real-Time Multimodal Signal Processing for HRI in RoboCup: Understanding a Human Referee
Filippo Ansalone, Flavio Maiorana, Daniele Affinita +8
Advancing human-robot communication is crucial for autonomous systems operating in dynamic environments, where accurate real-time interpretation of human signals is essential. Robo…
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…
Multi-Agent Planning Using Visual Language Models
Michele Brienza, Francesco Argenziano, Vincenzo Suriani +2
Large Language Models (LLMs) and Visual Language Models (VLMs) are attracting increasing interest due to their improving performance and applications across various domains and tas…
Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming
Mulham Fawakherji, Vincenzo Suriani, Daniele Nardi +1
The use of deep learning methods for precision farming is gaining increasing interest. However, collecting training data in this application field is particularly challenging and c…
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…
LLM Based Multi-Agent Generation of Semi-structured Documents from Semantic Templates in the Public Administration Domain
Emanuele Musumeci, Michele Brienza, Vincenzo Suriani +2
In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex…
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…
Enhancing Graph Representation of the Environment through Local and Cloud Computation
Francesco Argenziano, Vincenzo Suriani, Daniele Nardi
Enriching the robot representation of the operational environment is a challenging task that aims at bridging the gap between low-level sensor readings and high-level semantic unde…
Knowledge Acquisition and Completion for Long-Term Human-Robot Interactions using Knowledge Graph Embedding
E. Bartoli, F. Argenziano, V. Suriani +1
In Human-Robot Interaction (HRI) systems, a challenging task is sharing the representation of the operational environment, fusing symbolic knowledge and perceptions, between users…