Jinzhou Li

I am a Robotics Ph.D. student at Duke University, advised by Prof. Xianyi Cheng.

Prior to this, I obtained my master's degree from Cornell University, and bachelor's degree from the University of Vermont. I was fortunate to be working with Prof. Maha Haji at Cornell and Prof. Daniel Hastings at MIT. I also had the opportunity to work with Prof. Hao Dong at Peking University.

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News

  • [2025/06] Check out our new paper on ClutterDexGrasp!
  • [2025/06] Two papers get accepted to IROS 2025 as oral presentation! 🎉
  • [2025/05] I will attend ICRA 2025 conference in Atlanta!
  • [2025/03] I will be joining Duke University as a Ph.D. student in Fall 2025!
  • [2025/01] One paper accepted to ICRA 2025!

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Research

My research focuses on enabling robots to achieve human-level dexterity in complex environments by integrating multisensory intelligence with advanced control strategies and machine learning.

* Equal Contribution

ClutterDexGrasp: A Sim-to-Real System for General Dexterous Target Grasping in Cluttered Scenes

Zeyuan Chen*, Qiyang Yan*, Yuanpei Chen*, Tianhao Wu, Jiyao Zhang, Zihan Ding, Jinzhou Li, Yaodong Yang, Hao Dong
Preprint, 2025
paper, website, code

We propose the first close-loop sim-to-real system for general dexterous grasping in cluttered scenes.



TwinAligner: Visual and Physical Real2Sim2Real All-in-one for Robotic Manipulation

Hongwei Fan*, Hang Dai*, Jiyao Zhang*, Jinzhou Li, Qiyang Yan, Yujie Zhao, Yuxuan Lai, Hao Tang, Hao Dong
Preprint, 2025
paper, website

A novel Real2Sim2Real system addressing both visual and physics gaps.



AdapTac-Dex: Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation

Jinzhou Li*, Tianhao Wu*, Jiyao Zhang**, Zeyuan Chen**, Haotian Jin, Mingdong Wu, Yujun Shen, Yaodong Yang, Hao Dong
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
paper, website, code

A future force-guided attention fusion module that adaptively adjusts the weights of visual and tactile features.



SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training

Mingdong Wu*, Lehong Wu*, Yizhuo Wu*, Weiyao Huang, Hongwei Fan, Zheyuan Hu, Haoran Geng, Jinzhou Li, Jiahe Ying, Long Yang, Yuanpei Chen, Hao Dong
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025)
paper, website

We combine the strengths of real-world RL and real-to-sim-to-real approaches to accelerate policy learning.



Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning

Tianhao Wu, Jinzhou Li*, Jiyao Zhang*, Mingdong Wu, Hao Dong
IEEE International Conference on Robotics and Automation (ICRA 2025)
paper, website, code

A novel 3D tactile data representation and force-based pretraining to enhance dexterous manipulation learning.



Experience

  • Robotics Research Intern - AgiBot, 2025

    Working on robotic manipulation research.

Teaching

  • Teaching Assistant - Meta CS 4/5782: Intro to Deep Learning, Fall 2023, Cornell University

Talks

  • Invited Talk - Peking University, April 2025

    AdapTac: Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation

Professional Activities

  • Conference Reviewer: ICRA 2024, 2025


May, 2025.

Design and source code from Jon Barron's website.