Hi! I am an MSc student at Cranfield University studying Aerospace and Robotics.
My interests intersect with robotics and mechatronics, artificial intelligence and control system. I am currently working on Learning off-road vehicle traversability using Semantic Segmentation and Deep Reinforcement Learning.
Before going back to the University for my master studies, I have started my career by joining the Autonomous Driving Research team in SenseTime Group Limited as a Researcher Intern once I graduated from the Chinese University of Hong Kong in 2018. I was involved in researching and implementing different path planning and control algorithms, such as RRT-Star, Lattice Planner, Timed-Elastic-Band Trajectory Optimisation, PID control as well as Model Predictive Control. Besides, I was one of the main developers in the SenseRover Pro Project. SenseRover Pro is a 1:10 scale autonomous driving car that mimics the level-4 autonomous driving system, which was later commercialised as an educational product for different high schools in Hong Kong and Mainland China.
After working for a year, I switched to a more research-oriented job – Research Assistant at The Chinese University of Hong Kong. I was under the supervision of Prof. ZHOU Bolei in the Multimedia Lab (MMLab), which is one of the pioneering institutes on deep learning. My research focuses on Mobile Robot Navigation, Robot Arm Manipulation, and Deep Reinforcement Learning.
BENG in Mechanical and Automation Engineering, Minor in Computer Science, 2018
The Chinese University of Hong Kong
MSc Autonomous Vehicle Dynamics and Control, 2022
Cranfield University
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Conducting research related to Indoor Navigation and Robot-Arm Grasping. Responsibilities include:
Conducting research on Path Planning Algorithm of an Autonomous Driving Car. Participating the R&D process of a 1/10 scale autonomous driving car. Responsibilities include:
Responsibilities include:
This package is used for training and validating the Deep RL control policy on the LoCoBot, a low-cost robot equipped with a 5 DOF robotic arm on top of a differential-drive mobile base. Deep RL Policy can be trained and validated in both MuJoCo and Gazebo Simulator.
This project is my first project during my internship in SenseTime Corporation. It involved developing a 1:10 scale autonomous driving car capable of tracking double lane racetrack in real time and simple object detection. Solutions powered by OpenCV and Tensorflow had been adopted.