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feat: add Maeva Guerrier's profile and image to the people section + modify simon
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src/people/maeva-guerrier.md

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---
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name: Maeva Guerrier
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role: phd
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title: PhD Student
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photo: maeva-guerrier.jpg
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short_bio: Associated with Mila, she researches robot learning — visual navigation, foundation models, and reinforcement learning.
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supervisor: Jana Pavlasek & Giovanni Beltrame
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tags: [Robot Learning, Foundation Models, RL, World Models]
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order: 2
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---
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Maeva Guerrier is a PhD student at the MIST Lab, associated with Mila. She is
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supervised by Professor Jana Pavlasek and Professor Giovanni Beltrame.
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## Current Work
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Maeva's research focuses on **robot learning**, including:
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- **Visual navigation** — learning policies for autonomous navigation from visual observations.
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- **Foundation models** — leveraging large pre-trained models for robotic perception and control.
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- **Reinforcement learning** — training agents through interaction to solve complex robotic tasks.
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- **World models** — learned models of environment dynamics for planning and decision-making.

src/people/simon-roy.md

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---
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name: Simon Roy
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role: msc
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title: Master's Student
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role: phd
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title: PhD Student
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photo: simon-roy.jpg
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short_bio: Ad astra per scientiam — working on LLM-driven mission planning and vision-based precision landing for autonomous robots.
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short_bio: Ad astra per scientiam — working on world models, action compression for world models, and humanoid and space robotics.
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supervisor: Giovanni Beltrame
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tags: [Mission Planning, LLMs, Aerial Robotics]
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tags: [World Models, Action Compression, Humanoid Robotics, Space Robotics]
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email: simon-7.roy@polymtl.ca
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github: ""
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linkedin: https://www.linkedin.com/
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order: 1
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---
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Simon Roy is a Master's student in computer engineering at Polytechnique
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Simon Roy is a PhD student in computer engineering at Polytechnique
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Montréal and Mila, supervised by Professor Giovanni Beltrame. He received his
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Bachelor's degree with honors in Software Engineering from the École de
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technologie supérieure (ÉTS) in December 2024. He is interested in autonomous
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robotics and space exploration.
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## Current Work
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- **LLM-driven mission planning** — using large language models to translate
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high-level objectives into structured, adaptive task sequences for autonomous
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robots under real-world constraints.
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- **Precision landing for aerial robotics** — vision-based, markerless
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algorithms that let drones identify, track, and land on arbitrary targets with
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sub-meter accuracy.
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- **Latent-space world modeling** — compact learned representations of
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environmental dynamics for real-time planning and decision-making.
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- **World models** — learned representations of environmental dynamics for
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planning and decision-making in autonomous systems.
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- **Action compression for world models** — compact encodings of action spaces
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that make world-model-based planning tractable in real time.
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- **Humanoid robotics** — applying world models and compressed action spaces to
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planning and control for humanoid robots.
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## Contact
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