About Me

I am a Master’s student in Informatics at the University of Zurich (UZH), majoring in Artificial Intelligence and minoring in Data Science. My research interests lie at the intersection of Computer Vision, Robotics, and Multimodal Models.

Currently, I am a student researcher at:

Computer Vision and Geometry Group (ETHZ CVG), working on Real-time Visual-Inertial-LiDAR Gaussian Splatting SLAM.
Robotics and Perception Group (UZH RPG), focusing on Event Camera-Enhanced Robot Motor Control Policies.

I have experiences in:

Robotics & RL: Deep experience in RL/IL training and deployment on robot arms and drones.
3D Vision & SLAM: Expertise in Gaussian Splatting (3DGS), Visual Odometry, and SLAM pipelines.
Foundation Models: Familiarity with VLA models, LLMs, and multimodal pre-training.

Previously, I worked as a Computer Vision Algorithm Intern at IntSig Information Co., Ltd.

Feel free to reach out to me at jianwen.cao@uzh.ch.

Publications

[Accepted] Jianwen Cao, et al. Generative Event Pretraining with Foundation Model Alignment. CVPR 2026.
[Accepted, Oral] …, Jianwen Cao, et al. Memory Over Maps: 3D Object Localization Without Reconstruction. IEEE ICRA 2026.
[Published] Jianwen Cao, et al. Fuse Tune: Hierarchical Decoder Towards Efficient Transfer Learning. PRCV 2023.

Projects

SO-ARM Reinforcement Learning for Robot Manipulation

05.2026. Built a reinforcement learning pipeline for SO-ARM manipulation, covering simulation setup, policy training, and deployment-oriented evaluation.

Memory Over Maps: 3D Object Localization Without Reconstruction

09.2025 - 03.2026. [Accepted, Oral] IEEE ICRA 2026. Proposed a novel framework for 3D object localization that leverages foundation model memory instead of explicit map reconstruction.

Event Camera-Enhanced Robot Motor Control Policies

02.2025 - 02.2026. Conducted autoregressive pre-training on multimodal data, significantly improving the performance and robustness of downstream vision and control tasks.