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Maeva Guerrier

Doctorat - Polytechnique
Superviseur⋅e principal⋅e
Giovanni Beltrame
Co-supervisor
Jana Pavlasek
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
IAG (Intelligence Artificielle Générale)
Théorie de l'apprentissage automatique
GitHub

Publications

Can Vision Foundation Models Navigate? Zero-Shot Real-World Evaluation and Lessons Learned
Maeva Guerrier
Karthik Soma
Jana Pavlasek
Giovanni Beltrame
Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations. Despite growing … (voir plus)real-world deployment, existing evaluations rely almost exclusively on success rate, whether the robot reaches its goal, which conceals trajectory quality, collision behavior, and robustness to environmental change. We present a real-world evaluation of five state-of-the-art VNMs (GNM, ViNT, NoMaD, NaviBridger, and CrossFormer) across two robot platforms and five environments spanning indoor and outdoor settings. Beyond success rate, we combine path-based metrics with vision-based goal-recognition scores and assess robustness through controlled image perturbations (motion blur, sunflare). Our analysis uncovers three systematic limitations: (a) even architecturally sophisticated diffusion and transformer-based models exhibit frequent collisions, indicating limited geometric understanding; (b) models fail to discriminate between different locations that are perceptually similar, however some semantics differences are present, causing goal prediction errors in repetitive environments; and (c) performance degrades under distribution shift. We will publicly release our evaluation codebase and dataset to facilitate reproducible benchmarking of VNMs.
2026-03-25
arXiv (prépublication)
doi.org
arxiv.org
Learning Control Barrier Functions and their application in Reinforcement Learning: A Survey
Maeva Guerrier
Hassan Fouad
Giovanni Beltrame
Reinforcement learning is a powerful technique for developing new robot behaviors. However, typical lack of safety guarantees constitutes a … (voir plus)hurdle for its practical application on real robots. To address this issue, safe reinforcement learning aims to incorporate safety considerations, enabling faster transfer to real robots and facilitating lifelong learning. One promising approach within safe reinforcement learning is the use of control barrier functions. These functions provide a framework to ensure that the system remains in a safe state during the learning process. However, synthesizing control barrier functions is not straightforward and often requires ample domain knowledge. This challenge motivates the exploration of data-driven methods for automatically defining control barrier functions, which is highly appealing. We conduct a comprehensive review of the existing literature on safe reinforcement learning using control barrier functions. Additionally, we investigate various techniques for automatically learning the Control Barrier Functions, aiming to enhance the safety and efficacy of Reinforcement Learning in practical robot applications.
2024-04-21
ArXiv (prépublication)
doi.org
arxiv.org