I am an incoming PhD student at University of British Columbia, advised by Prof. Renjie Liao and Prof. Lele Wang.
Currently, I am a research and teaching assistant at Southern University of Science and Technology, supervised by Prof. Max Q.-H. Meng and Prof. Hong Zhang.
Previously, I was a visiting student at UC Berkeley advised by Prof. Koushil Sreenath, working on guide dog robot at Hybrid Robotics Lab. I obtained my bachelor’s degree at Harbin Institute of Technology in 2021 and worked closely with Prof. Haoyao Chen.
My research interest lies in the intersection of control, optimization, and machine learning with their applications to planning, perception, and decision-making for autonomous systems.
Bachelor of Engineering, Automation
GPA: 93.06/100 (ranking 1st/70)
Advisors: Prof. Haoyao Chen and Prof. Hao Xiong
Honor: National Scholarship, Dean's Award, First-class Undergraduate Academic Scholarship
Academic Exchange Student
Advisors: Prof. Koushil Sreenath
Southern University of Science and Technology, China
Advisors: Prof. Max Q-H. Meng
Noah's Ark Lab, Huawei, China
Advisors: Prof. Jianzhuang Liu
Hybrid Robotics Lab, UC Berkeley, USA
Advisors: Prof. Koushil Sreenath
* co-first author
Student Advisor | Submitted to ICRA 2023
We propose a novel guidance robot system with a comfort-based concept. To allow humans to be guided safely and more comfortably to the target position in complex environments, our proposed force planner can plan the forces experienced by the human with the force-based human motion model. And the proposed motion planner generate the specific motion command for robot and controllable leash to track the planned force. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios.
First Author | ICRA 2022
We present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception stragy and MPC-based planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated.
First Author | ICRA 2021
We propose a hybrid physical Human-Robot Interaction model that involves leash tension to describe the dynamical relationship in the robot-guiding human system. This hybrid model is utilized in a mixed-integer programming problem to develop a reactive planner that is able to utilize slack-taut switching to guide a blind-folded person to safely travel in a confined space. The proposed leash-guided robot framework is deployed on a Mini Cheetah quadrupedal robot and validated in experiments.
Collaboration | CASE 2021
We developed an end-to-end framework that enabled multi-modal transitions between walking and jumping skills. Using multi-phased collocation based nonlinear optimization, optimal trajectories were generated for the quadrupedal robot while avoiding obstacles and allowing the robot to jump through window-shaped obstacles. An integrated state machine, path planner, and jumping and walking controllers enabled the Mini-Cheetah to jump over obstacles and navigate previously nontraversable areas.
Co-First Author | AIM 2021
We first contribute a well design deep neural network (DNN) as a precise black-box kinematic model of the amphibious robot. Then, we design a DNN based nonlinear model predictive controller which obtains the robot’s real-time moving command by iterative optimization. The simulation results indicate the proposed controller is superior to the basic controller in the robot’s tracking efficiency and accuracy.
Student Advisor | IROS 2022
We proposed a plane-fitting based uneven terrain navigation framework(PUTN) which is designed for effectively navigating on uneven terrain. A new terrain assessment with plane-fitting to evaluate the traversability of the terrain is proposed. Combined with the informed-RRT* and this terrain assessment method, a new planning algorithm, PF-RRT*, is proposed. By using Gaussian Process, the traversability of the dense path is generated given the sample tree generated by PF-RRT*. The results verify the advantages of the PF-RRT* algorithm and the practicability of PUTN.