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Jian-Jian Jiang (蒋艰坚)
M.S. student
School of Computer Science and Engineering
Sun Yat-sen University
jiangjj35@mail2.sysu.edu.cn
Github
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Biography
I'm currently a first-year master student at Sun Yat-sen University, advised by Prof. Wei-Shi Zheng, where I cultivate the interest in research, and develop the scientific ability and taste of it.
Previously, I obtain my B.E. degree in Hunan University.
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Research Interests
My current research interests focus on the efficient and reliable learning of grasping skills while ensuring their deployability in the real world.
At the same time, I am actively studying how to facilitate the robot to obtain generalizable, precise and complex manipulation skills.
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Publications
Below are my publications. My first author works are highlighted.
(This page includes papers in arXiv, & means equal contribution, * refers to corresponding author.)
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AI Robotics
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Grasp as You Say: Language-guided Dexterous Grasp Generation
Yi-Lin Wei, Jian-Jian Jiang, Chengyi Xing, Xiantuo Tan, Xiao-Ming Wu, Hao Li, Mark Cutkosky, Wei-Shi Zheng*.
Neural Information Processing Systems (NeurIPS), 2024.
paper
We propose a novel task "Dexterous Grasp as You Say" (DexGYS), with a benchmark and a framework, enabling robots to perform dexterous grasping based on human commands expressed in natural language.
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Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection
Jia-Feng Cai, Zibo Chen, Xiao-Ming Wu, Jian-Jian Jiang, Yi-Lin Wei, Wei-Shi Zheng*.
Conference on Robot Learning (CoRL), 2024.
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paper
We propose a Real-to-Sim framework for 6-DoF Grasp detection, named R2SGrasp, with the key insight of bridging this gap in a real-to-sim way, and build a large-scale simulated dataset to pretrain our model to achieve great real-world performance.
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An Economic Framework for 6-DoF Grasp Detection
Xiao-Ming Wu&, Jiafeng Cai&, Jian-Jian Jiang, Dian Zheng, Yi-Lin Wei, Wei-Shi Zheng*
European Conference on Computer Vision (ECCV), 2024
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code
We propose a new economic grasping framework for 6-DoF grasp detection to economize the training resource cost and meanwhile maintain effective grasp performance, which consists of a novel label selection strategy and a focal module to enable it.
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