Katrina Ashton

I am a PhD student at the University of Pennsylvania, in the Department of Computer and Information Science, where I work on computer vision for robotics. I am co-advised by Prof. Kostas Daniilidis and Prof. Bernadette Bucher.

I have done interships with the Robotics and AI Institute and Seeing Machines. I received a Bachelor of Engineering (R&D, Hons.) in Mechatronics and a B.S. in Mathematical Modelling from the Australian National University, where I also worked as a research assistant with Prof. Jochen Trumpf and at the 3A Institute.

Email  /  Google Scholar

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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
arxiv / paper / website /

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
arxiv / paper /

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





Design and source code from Leonid Keselman's website