Charlotte Morissette

M.Sc. Computer Science, AI and Robotics | McGill University - Mila Québec AI Institute

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I am a master’s student in Computer Science at McGill University - Mila Québec AI Institute, advised by Professor Gregory Dudek. My research is primarily in robot learning, more specifically in modality fusion, tactile sensing, foundation models, reinforcement learning, HRI, and computer vision. More recently, my work has focused on efficient modality fusion in VLA models for robotic manipulation, with a particular emphasis on tactile sensing.

If our interests align, please feel free to reach out — I am always happy to discuss potential research or collaboration opportunities. I am currently looking for PhD positions, research internships, or roles starting in Fall 2026.

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Select Publications

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    Tactile Modality Fusion for Vision-Language-Action Models
    Charlotte Morissette, Amin Abyaneh, Wei-Di Chang, Anas Houssaini, David Meger, Hsiu-Chin Lin, Jonathan Tremblay, and Gregory Dudek
    2026
    Paper
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    Contractive diffusion policies: Robust action diffusion via contractive sampling with differential equations
    Amin Abyaneh, Charlotte Morissette, Mohamad Danesh, Anas Houssaini, David Meger, Gregory Dudek, and Hsiu-Chin Lin
    International Conference on Learning Representations (ICLR), 2026
    Paper | Website | Code
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    Visual-Tactile Inference of 2.5 D Object Shape From Marker Texture
    Affan Jilani, François Hogan, Charlotte Morissette, Gregory Dudek, Michael Jenkin, and Kaleem Siddiqi
    IEEE Robotics and Automation Letters (RA-L), 2024
    Paper
  4. hypernet2.png
    Hypernetworks for zero-shot transfer in reinforcement learning
    Sahand Rezaei-Shoshtari, Charlotte Morissette, Francois R Hogan, Gregory Dudek, and David Meger
    In AAAI Conference on Artificial Intelligence, 2023
    Paper | Website | Code
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    A Study of Human-Robot Handover through Human-Human Object Transfer
    Charlotte Morissette, Bobak H Baghi, Francois R Hogan, and Gregory Dudek
    Neural Information Processing Systems (NeurIPS), Human in the Loop Learning (HiLL) Workshop, 2022
    Paper