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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
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News
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ResearchMy 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. |
Experience
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Teaching
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Talks
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Professional Activities
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May, 2025. Design and source code from Jon Barron's website. |