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I am a Ph.D. student at School of Computer Science, Peking University, where I am honored to be advised by Prof. Yizhou Wang and co-advised by Prof. He Wang. My research interests span Robotics, 3D Vision and Machine Learning. My research objective is to endow embodied agents working in complex real-world scenes with generalizable manipulation, navigation and control capabilities. |
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- NEW [2024/12] Invited talk at TechBeat .
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| *: joint first author; † corresponding author(s) |
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Jiangran Lyu, Ziming Li, Xuesong Shi, Chaoyi Xu, Yizhou Wang†, He Wang† arXiv 2025 arXiv / project page This work propose Dynamics-Adaptive World Action Model (DyWA) , a novel framework that enhances action learning by jointly predicting future states while adapting to dynamics variations based on historical trajectories. |
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Yuxing Chen, Songlin Wei, Bowen Xiao, Jiangran Lyu, Jiayi Chen, Feng Zhu, He Wang† arXiv 2024 arXiv / project page This work explores robotic hanger insertion with a focus on diverse garments, leveraging a novel learning-based approach. |
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Jiangran Lyu, Yuxing Chen, Tao Du, Feng Zhu, Huiquan Liu, Yizhou Wang†, He Wang† Conference on Robot Learning (CoRL), 2024 Data Generation for Robotics@RSS, 2024 arXiv / project page / code We propose the first learning-based robotic paper cutting system. Experimental results demonstrate that our method achives performance comparable to human operation with a single hand under the same conditions. |
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Yingda Yin*, Jiangran Lyu*, Yang Wang, Haoran Liu, He Wang†, Baoquan Chen†, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025 arXiv / project page / code We propose a novel Rotation Laplace distribution for probabilistic rotation estimation. Rotation Laplace distribution is robust to the disturbance of outliers and enforces much gradient to the low-error region, resulting in new state-of-the-art performance over both probabilistic and non-probabilistic baselines. |
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Shijie Wang, Haojie Li†, Jiangran Lyu, Qi Tian, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Under Review arXiv / project page / code We propose a novel Visual Co-occurrence Discovery Network (VCDN) to discover semantic co-occurring relationships within objects and facilitate the inference of their compositional structures. |
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Ph.D. student in Computer Science Advisor: Prof. Yizhou Wang and Prof. He Wang 2023.9 - Present |
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B.E. from School of Software 2019.9 - 2023.7 Grades: 94.10 (Rank 1/119) |
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Website template courtesy of Jon Barron. |