Research
I'm interested in computer vision, machine learning and robotics. In particular, I mainly work on scene representations for robotics, along with modular and interpretable policies for performing tasks using these scene representations.
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Zero-shot Object-Centric Instruction Following: Integrating Foundation Models with Traditional Navigation
Sonia Raychaudhuri, Duy Ta, Katrina Ashton, Angel X. Chang, Jiuguang Wang, Bernadette Bucher
arXiv, 2025
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A zero-shot method to ground natural language instructions to a factor graph and a policy to use this for object-centric Vision-and-Language Navigation
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Multimodal LLM Guided Exploration and Active Mapping using Fisher Information
Wen Jiang*, Boshu Lei*, Katrina Ashton, Kostas Daniilidis
arXiv, 2024
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An active Gaussian Splatting SLAM system with active exploration strategy which balances the accuracy of mapping and localization
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Equivariant Filter for Feature-Based Homography Estimation for General Camera Motion
Tarek Bouazza, Katrina Ashton, Pieter van Goor, Robert Mahony, Tarek Hamel
Conference on Decision and Control (CDC), 2024
Homography estimation for arbitrary trajectories using the Equivariant Filter framework by exploiting the Lie group structure of SL(3)
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Unordered Navigation to Multiple Semantic Targets in Novel Environments
Bernadette Bucher*, Katrina Ashton*, Bo Wu, Karl Schmeckpeper, Nikolai Matni, Georgios Georgakis, Kostas Daniilidis
CVPR Embodied AI Workshop, 2023
Proposing an objective to extend an objectnav method to unordered multi-object navigation
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An observer for infinite dimensional 3d surface reconstruction that converges in finite time
Sean G. P. O’Brien, Katrina Ashton, Jochen Trumpf
IFAC-PapersOnLine, 2020
An observer for reconstructing the dense structure of scenes from visual or depth sensors that provably converges in finite time, demonstrated with real light-field camera data
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