Zhexi Luo

Zhexi Luo 羅喆熙

Hi there! 👋 I’m Zhexi. I’m currently doing robotics research at the National University of Singapore (NUS), where I’m advised by Prof. Lin Shao and work closely with Zhixuan Xu. Before that, I studied Computer Science and Technology at Sun Yat-sen University, graduating in June 2026.

My research focuses on dexterous manipulation and grasping, driven by two questions:

How can robots generalize across objects, tasks, and environments?
How can robots achieve human-level dexterity?

I explore how foundation models and human experience can help build generalist robots with these capabilities.

Outside of research, you’ll often find me surfing, hiking, playing badminton, backpacking, or spending time with my dog.

I’m looking for PhD opportunities starting in Fall 2027. If you’d like to chat about research, potential collaborations, or shared interests, I’d love to hear from you!

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📰 News

2026.6 ✈️ Attended ICRA 2026 in Vienna!
2025.11 🔬 Joined Lins Lab at the National University of Singapore as a research intern.
2025.10 🤖 Attended CoRL 2025, met many fascinating robots!
2025.4 🚀 Started my journey in robotics research

📝 Blog

Coming Soon!

📚 Publication

^* Equal contribution · ^† Corresponding author

	DexMimic: A Generalist Dexterous Hand–Object Tracking Policy via Value-Guided Reset Allocation Zhexi Luo, Zhixuan Xu, Rui Yang, Zeyu Xiao, Yiwen Hou, Lin Shao^† *Under Review · Sep. 2026* arXiv / PDF / project page One generalist policy for diverse dexterous skills from human demonstrations, with efficient learning and growing generalization as training scales. Bimanual Dexterity Human Demonstrations Reinforcement Learning
	OmniDexGrasp: Generalizable Dexterous Grasping via Foundation Model and Force Feedback Yi-Lin Wei^, Zhexi Luo^, Yuhao Lin, Mu Lin, Zhizhao Liang, Shuoyu Chen, Wei-Shi Zheng^† *ICRA 2026* arXiv / project page / code / media coverage Dexterous grasping across diverse user prompts, robot hands, and tasks, all within one generalizable framework. Foundation Model Dexterous Grasping Generalizable
	T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale Xin Fei^, Zhixuan Xu^, Huaicong Fang, Fangjun Chen, Mu Zhaoyu, Yang Rui, Zhexi Luo, Lin Shao^† *CoRL 2026* arXiv / project page / code Dexterous grasping across robot hands in cluttered scenes, with performance improving as models and data scale. Cross-Embodiment Dexterous Grasping Cluttered Environments
	Generalizable Humanoid Loco-Manipulation via Spatial Perception and Decision-Making of Multi-Agent Large Model Zhizhao Liang^, Yi-Lin Wei^, Xuhang Chen^, Mu Lin, Yi-Xiang He, Zhexi Luo, Jun-Hui Liu, Kun-Yu Lin, Wei-Shi Zheng^† ECCV 2026 (Oral)* arXiv / project page Generalizable whole-body mobile manipulation for humanoid robots, without task-specific real-robot data. Humanoid Loco-Manipulation Spatial Reasoning Multi-Agent Large Models
	AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation AXIS Robotics Team *IROS2026@PWMS* arXiv / project page Community-driven robot learning from browser-collected demonstrations, with a continually growing pool of manipulation data. Robot Learning Scalable Data Infra
	DriftTrace: Combating Concept Drift in Security Applications through Detection and Explanation Yuedong Pan, Lixin Zhao^†, Tao Leng, Zhexi Luo, Lijun Cai, Aimin Yu, Dan Meng *IEEE Transactions on Information Forensics and Security (T-IFS), 2026* IEEE Xplore Detecting and explaining concept drift in security applications, helping models adapt to changing data.