About
I received Ph.D. in ECE from Institute for Robotics and Intelligent Machines, Georgia Tech, advised by Patricio A. Vela. I also collaborate closely with Jonathan Tremblay, Stephen Tyree, Bowen Wen, and Stan Birchfield from NVIDIA Research. I am a practical roboticist, interested in combining robotics and computer vision together to solve the challenges in the real world. To be more specific, I am working on 6-DoF Object Pose Estimation and Tracking, Object Grasping, and Human-robot Interaction related topics. [View my CV (short version/long version)] (Updated in Feb. 2024).
I received B.E. in Automation from Southeast University in 2018, advised by Wenze Shao and Yangang Wang. I was also a research intern at Applied Nonlinear Control Lab, University of Alberta, in 2017, advised by Alan Lynch; NVIDIA Learning and Perception Research group from May 2020 to May 2021 and May 2022 to Dec 2022, advised by Stan Birchfield; Meta FAIR Accel Ego-HowTo team from May 2023 to November 2023, advised by Kevin Liang and Matt Feiszli.
News
- [July 2023] One paper accepted to ICCV 2023 about our work WDiscOOD Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
- [June 2023] One paper accepted to IROS 2023 about our work Keypoint-GraspNet v2
- [May 2023] Excited to join Meta FAIR Accel to work as a research intern
- [Jan 2023] Two papers accepted to ICRA 2023 about our work Parallel NeRF for Pose Estimation and Keypoint-GraspNet
- [June 2022] One paper accepted to RA-L with IROS 2022 option about our work Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following
- [May 2022] Great to come back to NVIDIA Learning and Perception Research Group to work as a research intern
- [Jan. 2022] Two papers accepted to ICRA 2022 about our work CenterPose and CenterPoseTrack
- [June 2021] One paper accepted to IROS 2021 about our work Multi-view Fusion for Multi-level Robotic Scene Understanding
- [Jan. 2021] One paper accepted to ICRA 2021 about our work A Joint Network for Grasp Detection Conditioned on Natural Language Commands
- [May 2020] Excited to join NVIDIA Learning and Perception Research Group to work as a research intern
- [Jan. 2020] One paper accepted to ICRA 2020 about our work Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Industry Experience
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Research Intern in FAIR Accel, 05/2023 - 11/2023
Meta
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Research Intern in Learning and Perception Research Group, 05/2022 - 12/2022 & 05/2020 - 05/2021
NVIDIA
Education
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Ph.D. in Electrical and Computer Engineering, 2024
Georgia Institute of Technology
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M.S. in Electrical and Computer Engineering, 2020
Georgia Institute of Technology
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B.E. in Automation, 2018
Southeast University
Featured Publications
Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation
A parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF target poses.
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty Estimation
A single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category.
Single-Stage Keypoint-based Category-level Object Pose Estimation from an RGB Image
A single-stage, keypoint-based approach for category-level object pose estimation that operates on unknown object instances within a known category using a single RGB image as input.
Multi-view Fusion for Multi-level Robotic Scene Understanding
A system for multi-level scene awareness for robotic manipulation, including three types of information: 1) a point cloud representation of all the surfaces in the scene, for the purpose of obstacle avoidance. 2) the rough pose of unknown objects from categories corresponding to primitive shapes (e.g., cuboids and cylinders), and 3) full 6-DoF pose of known objects.
