Zhanxin Wu

I am a CS PhD student at Cornell University. I worked on Open-World Object Goal Navigation across embodiments and environments at the NUS Adacomp.

Previously, I worked with Prof David Hsu at NUS. I also collaborated with Prof Jean Oh as a Robotics Institute Summer Scholar at Carnegie Mellon University and with Prof Junhua Zhao at CUHKSZ. I received my B.E. in Computer Science and Engineering from the Chinese University of Hongkong, Shenzhen (CUHKSZ) in 2022 and M.S. in Computer Science at National University of Singapore (NUS) in 2024.

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Our OpenSearch system is capable of searching for a specified object class, given open-set instructions, across diverse embodiments and environments. This is enabled by our Open Scene Graph, which acts as a scene memory for a fully Foundation Model-based (FM) system, that is itself purely built from FMs.

This work examines, experimentally and theoretically, one representation that enables visual navigation policies solely trained in the Habitat simulator to generalize to real-world scenes, both indoor and outdoors.

A framework that enables an agent to put misplaced objects back in place with partial map information by exploiting commonsense knowledge in large language models (LLMs).

A lightweight framework for semantic segmentation to recover information from uninformative pixels and improve 3D semantic understanding of scenes.

Formulated corporate carbon footprint estimation problem and proposed the first Methodology to estimate the direct and indirect carbon emissions of factories in real time

A temporal convolutional neural network with attention mechanism based method is proposed for industrial non-intrusive load monitoring.

Developed a deep end-to-end super resolution perception, recovered nearly 89% of high-frequency information lost in low-frequency information for equipment load identification at 1/10th of the original data volume, communication, and storage requirements.

Developed a self-driving agent in the Duckietown simulation with classical planning, computer vision, and imitation learning techniques. Placed among the top-scored project in the module.

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