1 code implementation • 26 Apr 2024 • Pengwei Xie, Rui Chen, Siang Chen, Yuzhe Qin, Fanbo Xiang, Tianyu Sun, Jing Xu, Guijin Wang, Hao Su
Manipulating unseen articulated objects through visual feedback is a critical but challenging task for real robots.
no code implementations • 22 Feb 2024 • Jun Wang, Yuzhe Qin, Kaiming Kuang, Yigit Korkmaz, Akhilan Gurumoorthy, Hao Su, Xiaolong Wang
We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks.
no code implementations • 23 Jan 2024 • Kang-Won Lee, Yuzhe Qin, Xiaolong Wang, Soo-Chul Lim
In this paper, we introduce a multi-finger robot system designed to search for and manipulate objects using the sense of touch without relying on visual information.
no code implementations • 4 Dec 2023 • Ying Yuan, Haichuan Che, Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Kang-Won Lee, Yi Wu, Soo-Chul Lim, Xiaolong Wang
In this paper, we introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation.
1 code implementation • 2 Oct 2023 • Lirui Wang, Yiyang Ling, Zhecheng Yuan, Mohit Shridhar, Chen Bao, Yuzhe Qin, Bailin Wang, Huazhe Xu, Xiaolong Wang
Collecting large amounts of real-world interaction data to train general robotic policies is often prohibitively expensive, thus motivating the use of simulation data.
no code implementations • 11 Sep 2023 • Binghao Huang, Yuanpei Chen, Tianyu Wang, Yuzhe Qin, Yaodong Yang, Nikolay Atanasov, Xiaolong Wang
Humans throw and catch objects all the time.
no code implementations • 10 Jul 2023 • Yuzhe Qin, Wei Yang, Binghao Huang, Karl Van Wyk, Hao Su, Xiaolong Wang, Yu-Wei Chao, Dieter Fox
For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot.
no code implementations • CVPR 2023 • Chen Bao, Helin Xu, Yuzhe Qin, Xiaolong Wang
On the other hand, operating with a multi-finger robot hand will allow better approximation to human behavior and enable the robot to operate on diverse articulated objects.
no code implementations • 2 May 2023 • Zehao Zhu, Jiashun Wang, Yuzhe Qin, Deqing Sun, Varun Jampani, Xiaolong Wang
We propose a new dataset and a novel approach to learning hand-object interaction priors for hand and articulated object pose estimation.
no code implementations • 2 May 2023 • Linghao Chen, Yuzhe Qin, Xiaowei Zhou, Hao Su
Hand-eye calibration is a critical task in robotics, as it directly affects the efficacy of critical operations such as manipulation and grasping.
no code implementations • 20 Mar 2023 • Zhao-Heng Yin, Binghao Huang, Yuzhe Qin, Qifeng Chen, Xiaolong Wang
Relying on touch-only sensing, we can directly deploy the policy in a real robot hand and rotate novel objects that are not presented in training.
no code implementations • 17 Nov 2022 • Yuzhe Qin, Binghao Huang, Zhao-Heng Yin, Hao Su, Xiaolong Wang
We empirically evaluate our method using an Allegro Hand to grasp novel objects in both simulation and real world.
1 code implementation • 14 Oct 2022 • Stone Tao, Xiaochen Li, Tongzhou Mu, Zhiao Huang, Yuzhe Qin, Hao Su
In the abstract environment, complex dynamics such as physical manipulation are removed, making abstract trajectories easier to generate.
1 code implementation • 11 Jul 2022 • Jianglong Ye, Jiashun Wang, Binghao Huang, Yuzhe Qin, Xiaolong Wang
We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF.
no code implementations • 26 Apr 2022 • Yuzhe Qin, Hao Su, Xiaolong Wang
We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand.
1 code implementation • 29 Sep 2021 • Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang, Marcin Kierebinski, Ruihan Yang, Yuzhe Qin, Xiaolong Wang
While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world.
1 code implementation • 12 Aug 2021 • Yuzhe Qin, Yueh-Hua Wu, Shaowei Liu, Hanwen Jiang, Ruihan Yang, Yang Fu, Xiaolong Wang
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
1 code implementation • 29 Jun 2021 • Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, Leonidas Guibas
Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e. g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks.
no code implementations • 28 Jun 2021 • Quan Vuong, Yuzhe Qin, Runlin Guo, Xiaolong Wang, Hao Su, Henrik Christensen
We propose a teleoperation system that uses a single RGB-D camera as the human motion capture device.
1 code implementation • ICCV 2021 • Yijia Weng, He Wang, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas J. Guibas
For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories.
no code implementations • 11 Mar 2021 • Yuzhe Qin, Huaxiong Huang, Yi Zhu, Chun Liu, Shixin Xu
Numerical simulations first illustrate the consistency of theoretical results on the sharp interface limit.
Numerical Analysis Numerical Analysis 76Z99, 92B05, 76R50
1 code implementation • CVPR 2020 • Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su
To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.
1 code implementation • 31 Oct 2019 • Yuzhe Qin, Rui Chen, Hao Zhu, Meng Song, Jing Xu, Hao Su
Grasping is among the most fundamental and long-lasting problems in robotics study.
no code implementations • ICLR 2020 • Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, Michael C. Yip
The composition of elementary behaviors to solve challenging transfer learning problems is one of the key elements in building intelligent machines.