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.
Please feel free to reach out! Schedule a meeting with me !
jinzhou.li [at] duke [dot] edu
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 !
[2024/05] One paper accepted to ROMAN 2024 in Los Angeles!
[2024/03] I join Peking University as a visiting student, advised by Prof. Hao Dong .
[2023/12] One presentation poster accepted to MRS 2023 in Boston!
[2023/12] Graduated from Cornell University!
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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
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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.
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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.
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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.
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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.
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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.
Talks
Invited Talk - Peking University, April 2025
AdapTac: Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation