Hanchen Cui

I am a Ph.D. student in computer science at University of Minnesota Twin Cities, working with Prof. Karthik Desingh. I received my master's and bachelor's degree from Shanghai Jiao Tong University and Xidian University, respectively. Previously, I'm a researcher at Shanghai Qi Zhi Institute, where I work closely with Prof. Yang Gao at IIIS, Tsinghua University. Afterwards, I have a short visit at NUS, collaborating with Prof. Lin Shao

My research interest lies in robot learning and my long-term goal is to build a generalized robot that can accomplish long-horizon and complex tasks. To be mroe specific, I work on vision-language-action models, world models, representation learning, and legged locomotion.

I am interning at Meta FAIR this summer, working on robot learning.

Email: hanchen.cui147[at]gmail.com

Email / Scholar / LinkedIn

Publications

(representative papers are highlighted)

	Fine-Tuning Hard-to-Simulate Objectives for Quadruped Locomotion: A Case Study on Total Power Saving Ruiqian Nai, Jiacheng You, Liu Cao, Hanchen Cui, Shiyuan Zhang, Huazhe Xu, Yang Gao ICRA 2025 project page / pdf We propose a data-driven framework for fine-tuning locomotion policies, targeting these hard-to-simulate objectives. Our framework leverages real-world data to model these objectives and incorporates the learned model into simulation for policy improvement.
	A Universal World Model Learned from Large Scale and Diverse Videos Hanchen Cui, Yang Gao NeurIPS 2023, Foundation Models for Decision Making Workshop pdf / poster We propose a generalizable world model pre-tained by large scale and diverse video dataset and then fine-tune the world model to obtain an accurate dynamic function in a sample-efficient manner.
	A Universal Semantic-Geometric Representation for Robotic Manipulation Tong Zhang, Yingdong Hu, Hanchen Cui,Hang Zhao, Yang Gao CoRL 2023 project page / arXiv / code We present Semantic-Geometric Representation (SGR), a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning.

Modified version of template from here.