News
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- [Sep. 2024]:Our paper Maniwhere is accepted by CoRL 2024.
- [Jul. 2024]:Our paper DiffTactile won best paper award in Noosphere workshop at RSS 2024.
- [Jul. 2024]:Our paper Robo-ABC is accepted by ECCV 2024.
- [May. 2024]:Our paper 3D Diffusion Policy is accepted by RSS 2024.
- [Jan. 2024]:One paper ArrayBot is accepted by ICRA 2024.
- [Jan. 2024]:Two papers are accepted by ICLR 2024, one is spotlight.
- [Sep. 2023]:One paper is accepted by NeurIPS 2023.
- [Jun. 2023]:One paper Flexible Handover is accepted by IROS 2023.
- [Apr. 2023]:One paper IOT-CL is accepted by ICML 2023.
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Publications
* indicates equal contributions. Representative papers are highlighted.
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Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Zhecheng Yuan*,
Tianming Wei*,
Shuiqi Cheng,
Gu Zhang,
Yuanpei Chen,
and Huazhe Xu
Conference on Robot Learning(CoRL), 2024
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In this paper, we propose Maniwhere, a generalizable framework tailored for visual reinforcement learning, enabling the trained robot policies to generalize across a combination of multiple visual disturbance types.
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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
Robotics: Science and Systems (RSS), 2024
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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
Best Paper Award, RSS Noosphere (Tactile Sensing) Worshop, 2024
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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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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]
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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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Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation
Yuanchen Ju*,
Kaizhe Hu*,
Guowei Zhang,
Gu Zhang,
Mingrun Jiang,
and Huazhe Xu
European Conference on Computer Vision (ECCV), 2024
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In this paper, we present Robo-ABC, a framework through which robots can generalize to manipulate out-of-category objects in a zero-shot manner without any manual annotation, additional training, part segmentation, pre-coded knowledge, or viewpoint restrictions.
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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
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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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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]
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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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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
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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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Selected Awards and Honors
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- 2024: Best Paper Award in Noosphere workshop at RSS 2024
- 2024: Best Bachelor Thesis Award of Shanghai Jiao Tong University
- 2024: Shanghai Outstanding Graduates
- 2024: Zhiyuan Outstanding Scholarship (Top 30 winnners in Zhiyuan Honor College, ¥20000 RMB)
- 2023: SenseTime Scholarship (Top30 undergraduate AI researchers nationwide, ¥20000 RMB)
- 2023: Shanghai Scholarship (Top 0.2% Shanghai, ¥8000 RMB)
- 2022: National Scholarship (Top 0.2% nationwide, ¥8000 RMB)
- 2022: Hanying Juhua Scholarship (Top 15 winners in Zhiyuan Honor College, ¥15000 RMB)
- 2021: National Scholarship (Top 0.2% nationwide, ¥8000 RMB)
- 2021, 2022: A-level Merit Scholarship (Top 1% SJTU, ¥1500 RMB)
- 2021, 2022: Merit Student (Top 5% SJTU)
- 2020, 2021, 2022, 2023: Zhiyuan Honor Scholarship (Top 5% SJTU, ¥5000 RMB)
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Service
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- Reviewer: CoRL 2024, RA-L 2024, IJCAI 2024
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