3 citations · 3 across the 10 of their papers we have counts for
10 papers
FlowMaps: Modeling Long-Term Multimodal Object Dynamics with Flow Matching
Francesco Argenziano, Miguel Saavedra-Ruiz, Sacha Morin +3
Joint spatial and temporal understanding of 3D scenes is a crucial requirement for robots deployed in everyday household environments. Such agents must not only comprehend and navi…
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi, Francesco Argenziano, Sebastian Koch +10
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Thei…
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…
Agentic Scene Policies
Sacha Morin, Kumaraditya Gupta, Mahtab Sandhu +4
Designing or learning robot policies that generalize zero-shot across a range of language instructions and objects is a core problem in robotics. Vision-Language-Action models (VLA…
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…
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…
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…
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…