Ruixiang Wang

Ph.D. Student · CUHK-SZ

[Google Scholar] [GitHub]

About

I am a Ph.D. student in Computer Science at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), advised by Prof. Kui Jia. Previously, I worked with Prof. Guyue Zhou at the Institute for AI Industry Research (AIR), Tsinghua University. I also conducted research at King Abdullah University of Science and Technology (KAUST) under the supervision of Prof. Mohamed Elhoseiny and worked as a Research Intern at DexForce Technology. Before that, I received my B.Eng. in Automation with honors from Harbin Institute of Technology, Weihai.

My research focuses on building embodied intelligence for perceiving and interacting with the physical world. In particular, I am interested in developing generalizable robotic manipulation policies and learning world models that capture how the physical world appears, behaves, and evolves.

News

  • [2026.06] Our team won first place in the WorldArena Challenge at CVPR 2026. Champion
  • [2026.06] One paper was accepted to IROS 2026.
  • [2026.04] YOTO++ was accepted to IEEE TPAMI.
  • [2026.03] EVA was released and open-sourced.
  • [2025.10] One paper was accepted to IEEE Robotics and Automation Letters (RA-L).
  • [2025.09] I started my Ph.D. at The Chinese University of Hong Kong, Shenzhen.
  • [2025.06] GAT-Grasp was accepted to IROS 2025.
  • [2025.06] One paper was accepted to ICCV 2025.
  • [2025.04] YOTO was accepted to RSS 2025.

Education

Experience

Publications

Vid2WAM framework overview

Vid2WAM: Distilling Video Diffusion Priors into World Action Models

Chenhao Qiu*, Ruixiang Wang*†, Runyi Zhao*, Sixu Lin, Songen Gu, Shufeng Nan, Guiliang Liu, Kui Jia, Yanwei Fu, Simo Wu (* equal contribution · † project lead)

arXiv preprint, 2026

[Paper] [Project Page]

EVA overview

EVA: Aligning Video World Models with Executable Robot Actions via Inverse Dynamics Rewards

Ruixiang Wang, Qingming Liu, Yueci Deng, Guiliang Liu, Zhen Liu, Kui Jia

arXiv preprint, 2026

[Paper] [Project Page] [Code]

YOTO++ system and task overview

YOTO++: Learning Long-Horizon Closed-Loop Bimanual Manipulation from One-Shot Human Video Demonstrations

Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026

[Paper] [Project Page] [Code]

YOTO teaser

You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations

Huayi Zhou, Ruixiang Wang, Yunxin Tai, Yueci Deng, Guiliang Liu, Kui Jia

Robotics: Science and Systems (RSS), 2025

[Paper] [Project Page] [Code]

GAT-Grasp overview

GAT-Grasp: Gesture-Driven Affordance Transfer for Task-Aware Robotic Grasping

Ruixiang Wang, Huayi Zhou, Xinyue Yao, Guiliang Liu, Kui Jia

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025

[Paper] [arXiv]

Research Interests

01

Robot Manipulation

Generalizable policies for reliable, long-horizon manipulation.

02

World Models

Predictive models that remain physically grounded and executable.

03

Embodied Intelligence

Connecting perception, imagination, and action in the physical world.