about

About Me

My name is Meiqi “Qi” Zhao, an aspiring roboticist and Master’s student at Columbia University. I’m currently a part of the Google Summer of Code 2023 program under JdeRobot. As a student researcher, I am actively engaged in robotics research at the Creative Machines Lab at Columbia. In my spare time, I love hiking, traveling, and doing archery. Thank you for stopping by, and I hope you find my work engaging!

Mentors

Sergio Paniego Blanco, Nikhil Paliwal

Google Summer of Code

Google Summer of Code (GSoC) is a global program that brings student developers into open source software development. It offers students stipends to work on a three-month programming project under the guidance of expert mentors from participating open-source organizations. GSoC has been instrumental in introducing students to the vast world of open-source software, fostering innovative thinking, and facilitating the creation of various essential software tools.

JdeRobot

JdeRobot is an open-source toolkit that simplifies the complex task of building robotics applications. It promotes the integration of existing nodes or libraries, and provides various tools, libraries, and reusable nodes for robotics, artificial intelligence, and computer vision. It is fully compatible with the Robot Operating System (ROS) and supports multiple languages, including C++, Python, and JavaScript. JdeRobot is particularly renowned for its contributions to robotics education and game development, machine learning in robotics, and reconfigurable computing in robotics. Checkout their ongoing projects here!

Behavior Metrics

Behavior Metrics is an evaluation tool for autonomous driving models developed as part of the JdeRobot suite. It uses the Gazebo and CARLA simulator to create realistic race track and urban environments and situations for testing and benchmarking the performance of autonomous driving algorithms. By providing a range of performance metrics, Behavior Metrics allows developers to quantitatively assess and compare the efficacy of different driving algorithms and strategies. In the context of my GSoC project, Behavior Metrics serves as a platform for developing and evaluating an autonomous driving model that utilizes segmentation-based deep learning models for obstacle avoidance.