I am an ECE Ph.D. candidate advised by Patricio A. Vela at Institute for Robotics and Intelligent Machines, Georgia Tech. 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, Object Grasping, and Human-robot Interaction related topics. [View my CV] (Updated in Jan. 2023).
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.
Ph.D. in Electrical and Computer Engineering, 2023 (Expected)
Georgia Institute of Technology
M.S. in Electrical and Computer Engineering, 2020
Georgia Institute of Technology
B.E. in Automation, 2018
Southeast University
A parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF target poses.
A single-stage, category-level 6-DoF pose estimation algorithm that simultaneously detects and tracks instances of objects within a known category.
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.
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.