Zhenyu Wei

Zhenyu Wei | 卫振宇

I am a fourth-year undergraduate student majoring in Computer Science at Shanghai Jiao Tong University. Currently, I am fortune to be a research assistant at National University of Singapore advised by Prof. Lin Shao. Previously, I was an intern at Shanghai Jiao Tong University MVIG advised by Prof. Cewu Lu.

I'm looking for a Ph.D. position starting in Fall 2025. Please feel free to contact me!

Email / CV / Github / Google Scholar / WeChat

News

[2024/12] 🎉 IRC was accepted to TPAMI.
[2024/11] 🏅 $\mathcal{D(R, O)}$ Grasp won the Best Robotics Paper Award at CoRL 2024 MAPoDeL Workshop!

Research

My research interests lie in 🤖robot learning and 🦾dexterous manipulation. My long-term goal is to make robots an essential part of daily life, reshaping how we live. I'm open to collaborations on robotics related projects! If you are a researcher looking for a partner, feel free to contact me!🤟

Papers sorted by recency. Representative papers are highlighted.

	TelePreview: A User-Friendly Teleoperation System with Virtual Arm Assistance for Enhanced Effectiveness Jingxiang Guo, Jiayu Luo, Zhenyu Wei, Yiwen Hou, Zhixuan Xu, Xiaoyi Lin, Chongkai Gao, Lin Shao Website / arXiv / Code (Coming soon) Under review TL;DR:* Implement a low-cost teleoperation system utilizing data gloves and IMU sensors, paired with an assistant module that improves data collection process by visualizing future robot operations through visual previews.
	$\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping Zhenyu Wei, Zhixuan Xu, Jingxiang Guo, Yiwen Hou, Chongkai Gao, Zhehao Cai, Jiayu Luo, Lin Shao Website / arXiv / Code / Media In submission to ICRA 2025 CoRL 2024 @ MAPoDeL Best Robotics Paper Award (Oral Presentation) CoRL 2024 @ LFDM Spotlight Presentation × TL;DR: Introduce $\mathcal{D(R,O)}$, a novel interaction-centric representation for dexterous grasping tasks that goes beyond traditional robot-centric and object-centric approaches, enabling robust generalization across diverse robotic hands and objects.
	Auto-Pairing Positives through Implicit Relation Circulation for Discriminative Self-Learning Bo Pang, Zhenyu Wei, Jingli Lin, Cewu Lu IEEE / Code TPAMI IEEE Transactions on Pattern Analysis and Machine Intelligence TL;DR: Introduce Implicit Relation Circulation (IRC), a self-supervised learning framework leveraging random walks across feature groups without explicit matching. By connecting it to a simpler task, cycle consistency enables the automatic discovery of positive pairs.