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 world models, vision-language-action models, and multimodal representation learning.

I interned at Meta FAIR in the summer of 2025, where I was mentored by Franziska Meier and Krishna Murthy Jatavallabhula and worked on action-conditioned world models and vision-language-action models.

Email: hanchen.cui147[at]gmail.com

Email / Scholar / LinkedIn

Publications

* indicates equal contribution and † indicates joint last author. Representative work is highlighted.

	DREAMSTEER: Latent World Models can Steer VLA Policies during Deployment Without Any Finetuning Hanchen Cui, Sergio Arnaud, Arjun Majumdar, Daniel Dugas, Elie Aljalbout, Karthik Desingh^†, Krishna Murthy Jatavallabhula^† Franziska Meier^† Under review project page / pdf We propose DREAMSTEER, a plug-and-play deployment-time steering framework that enhances pretrained VLA policies without any finetuning, or any in-domain data. By imagining candidate futures with a latent world model and ranking them with a value model, DREAMSTEER enables robots to select more reliable action sequences before execution.
	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 / arXiv 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 / code We propose a generalizable world model pre-trained by large scale and diverse video dataset using latent action extracted by VQ-VAE and then fine-tune the world model by robot data to obtain an accurate dynamic function.
	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.

Research projects

A real-time vision-language manipulation system

(1) Build a vision-language manipulation deployment system to accomplish a variety of real world tasks based on Franka Panda Robot.

(2) A robust client-server architecture is employed to enable low-latency remote control.

(3) Fine-tune a vision-language manipulation model (CLIPORT), achieving 90% success rate in real-world experiments.

Legged robots learn from physical world

(1) Establish a sim2real locomotion deployment codebase for legged robots.

(2) Develop a real-world learning framework that acquires data from real-world interactions and performs policy training on a remote server.

Visual planning for legged robots

(1) Leverage the visual understanding and high-level planning abilities of large multimodal models (GPT4o), enabling legged robots to accomplish multi-step and complex tasks, such as sending packages, and objects retrieval.

Services

Reviewers: ICRA, RA-L, IROS

Modified version of template from here.