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 in Artificial Intelligence Class 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 /

Publications

^* indicates equal contributions.

	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 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.
	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).
	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.
	Thin-Shell Object Manipulations With Differentiable Physics Simulations Yian Wang^, Juntian Zheng^, Zhehuan Chen, Xian Zhou, Gu Zhang, Chao Liu, and Chuang Gan Conference on Robot Learning (CoRL), Workshop L4SR , 2023 paper coming soon In this paper, we introduce ThinShellLab, a fully differentiable simulation platform tailored for diverse thin-shell material interactions with varying properties.
	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 Arxiv, 2023 project page / paper 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.

Selected Awards and Honors

2023: Shanghai Scholarship (8000RMB¥)
2022: National Scholarship (8000RMB¥)
2022: Hanying Juhua Scholarship (15 winners, 15000RMB¥)
2021: National Scholarship (8000RMB¥)
2021-2022: A-level Excellent Scholarship of SJTU

Academic Performance

GPA: 94.47/100 (or 4.12/4.3), Rank 1/88
Achieved A+ in more than 30 Courses.

Design and source code from Jon Barron's website.