I am a researcher in the Machine Learning Department at Carnegie Mellon University, advised by Prof. Christopher Atkeson and Prof. Katerina Fragkiadaki.
I received my bachelor degree in Computer Science from Zhejiang University, with High Honors and minor in Electronic Engineering.
My work is
Robotics, Machine Perception, Machine Learning, especially the intersection of them.
Scene / Object Simulation and Understanding
Human Activity Sensing and Understanding
SAPIEN: A SimulAted Part-based Interactive ENvironment
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
[Paper] [Project] [Video]
We build SAPIEN simulator: an interaction-rich and physics-realistic simulation environment integrating PhysX engine and ROS control interface. I lead the team of building SAPIEN dataset: more than 2K 3D articulated models with 14K movable parts. The dataset is richly annotated with kinematic part motions and dynamic interactive attributes to support robot interaction.
S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes
Yuzhe Qin*, Rui Chen*, Hao Zhu, Meng Song, Jing Xu, Hao Su (*Equal contribution)
[Paper] [Project] [Video] [Presentation]
We studied the problem of 6-DoF grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single view point. Our learning based approach trained in a synthetic scene can work well in real-world scenarios, with improved speed and success rate compared with state-of-the-arts.
We collect CrowdPose, a new dataset of crowded human poses. We propose a joint-candidate single person pose estimation (SPPE) and a global maximum joints association algorithm to tacklethe problem of pose estimation in a crowd. Our method surpasses the state-of-the-art method by 5.2 mAP on CrowdPose and replacing certain steps in the state-of-the-art method with our module would bring 0.8 mAP improvement on MSCOCO.