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Gu Zhang I am an incoming Ph.D. student in Computer Science at Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University under the supervision of Prof. Huazhe Xu. Currently, I am an undergraduate at Shanghai Jiao Tong University (SJTU) with GPA ranking 1/88. My research interest mainly lie in robotics and machine learning, with a particular focus on robotic manipulation and multisensory learning. During my undergraduate study, I am fortunate to be mentored by Prof. Cewu Lu and Prof. Junchi Yan. I am also a research visiting student/visitor at Massachusetts Institute of Technology and MIT-IBM Watson AI Lab under the supervision of Prof. Josh Tenenbaum and Prof. Chuang Gan. Email / Google Scholar / Github / WeChat / Twitter / |
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Publications* indicates equal contributions. |
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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
Yanjie Ze*, Gu Zhang*, Kangning Zhang, Chenyuan Hu, Muhan Wang and Huazhe Xu Arxiv, 2024 project page / paper / code / twitter In this paper, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. |
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DIFFTACTILE: A Physics-based Differentiable Tactile Simulator for Contact-rich Robotic Manipulation
Zilin Si*, Gu Zhang*, Qingwei Ben*, Branden Romero, Xian Zhou, Chao Liu and Chuang Gan International Conference on Learning Representations (ICLR), 2024 project page / paper / code / twitter In this paper, we introduce DIFFTACTILE, a physics-based and fully differentiable tactile simulation system designed to enhance robotic manipulation with dense and physically-accurate tactile feedback. |
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Flexible Handover with Real-Time Robust Dynamic Grasp Trajectory Generation
Gu Zhang, Hao-shu Fang, Hongjie Fang and Cewu Lu IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 [Oral] paper In this paper, we propose an approach for effective and robust flexible handover, which enables the robot to grasp moving objects with flexible motion trajectories with a high success rate. |
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Thin-Shell Object Manipulations With Differentiable Physics Simulations
Yian Wang*, Juntian Zheng*, Zhehuan Chen, Xian Zhou, Gu Zhang, Chao Liu, and Chuang Gan International Conference on Learning Representations (ICLR), 2024 [Spotlight] project page / paper In this paper, we introduce ThinShellLab, a fully differentiable simulation platform tailored for diverse thin-shell material interactions with varying properties. |
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ArrayBot: Reinforcement Learning for Generalizable Distributed Manipulation through Touch
Zhengrong Xue*, Han Zhang*, Jingwen Chen, Zhengmao He, Yuanchen Ju, Changyi Lin, Gu Zhang, and Huazhe Xu International Conference on Robotics and Automation(ICRA), 2024 project page / paper / code In this paper, we present ArrayBot, a distributed manipulation system consisting of a 16×16 array of vertically sliding pillars integrated with tactile sensors, which can simultaneously support, perceive, and manipulate the tabletop objects. |
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Understanding and Generalizing Contrastive Learning from the Inverse Optimal Transport Perspective
Liangliang Shi, Gu Zhang, Haoyu Zhen, and Junchi Yan International Conference on Machine Learning (ICML), 2023 paper In this paper, we aim to understand CL with a collective point set matching perspective and formulate CL as a form of inverse optimal transport (IOT). |
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Relative Entropic Optimal Transport: a (Prior-aware) Matching Perspective to (Unbalanced) Classification
Liangliang Shi, Haoyu Zhen, Gu Zhang, and Junchi Yan Conference on Neural Information Processing Systems (NeurIPS), 2023 paper In this paper, we propose a new variant of optimal transport, called Relative Entropic Optimal Transport (RE-OT) and verify its effectiveness for inhancing visual learning. |
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Design and source code from Jon Barron's website. |