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Francesco Argenziano

Alumni
Google Scholar

Publications

FlowMaps: Modeling Long-Term Multimodal Object Dynamics with Flow Matching
Francesco Argenziano
Miguel Saavedra-Ruiz
Sacha Morin
Charlie Gauthier
Daniele Nardi
Liam Paull
Joint spatial and temporal understanding of 3D scenes is a crucial requirement for robots deployed in everyday household environments. Such … (see more)agents must not only comprehend and navigate spatial layouts, but also reason about how these spaces evolve over time. In particular, humans interact with objects daily, causing them to change position throughout the environment and making it difficult for robots to reliably associate current observations with previously seen objects. However, these interactions are not random: human habits and routines induce spatio-temporally consistent patterns in object locations, which robotic agents can potentially learn and then exploit for downstream tasks such as navigation. To this end, we introduce FlowMaps, a latent flow matching model for estimating multimodal distributions over the future locations of dynamic objects in a continuous 3D space. By learning the implicit dependencies among objects and their temporal evolution, FlowMaps predicts likely changes in object locations conditioned on past human interactions, while supporting generalization across previously unseen environments that share similar object routines. To demonstrate the utility of this method, we deploy FlowMaps in a downstream dynamic Object Navigation task in both simulated and real-world environments. Across more than 600 episodes, FlowMaps outperforms state-of-the-art approaches, showing that modeling object dynamics through continuous, multimodal spatio-temporal distributions improves robotic search and navigation in changing household environments. Code and additional material is available at https://fra-tsuna.github.io/flowmaps/.
2026-06-17
arXiv (preprint)
doi.org
arxiv.org
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Buechner
Johanna Wald
Lukas Rosenberger Schmid
Daniele Nardi
Abhinav Valada
Liam Paull
Federico Tombari
Luca Carlone
Kai O. Arras
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relation… (see more)al abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including manipulation, navigation, task planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real-world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are built from raw sensory observations, discussing the most common terminologies, conventions, and techniques. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed content, accessible at https://3dscenegraphs.com/.
2026-06-14
arXiv (preprint)
doi.org
arxiv.org
Object-Centric Agentic Robot Policies
Sacha Morin
Kumaraditya Gupta
Mahtab Sandhu
Charlie Gauthier
Francesco Argenziano
Kirsty Ellis
Liam Paull
Executing open-ended natural language queries in previously unseen environments is a core problem in robotics. While recent advances in imit… (see more)ation learning and vision-language modeling have enabled promising end-to-end policies, these models struggle when faced with complex instructions and new scenes. Their short input context also limits their ability to solve tasks over larger spatial horizons. In this work, we introduce OCARP, a modular agentic robot policy that executes user queries by using a library of tools on a dynamic inventory of objects. The agent builds the inventory by grounding query-relevant objects using a rich 3D map representation that includes open-vocabulary descriptors and 3D affordances. By combining the flexible reasoning abilities of an agent with a general spatial representation, OCARP can execute complex open-vocabulary queries in a zero-shot manner. We showcase how OCARP can be deployed in both tabletop and mobile settings due to the underlying scalable map representation.
2025-09-22
NeurIPS.cc/2025/Workshop/SpaVLE (poster)
openreview.net
openreview.net