Jason Gibson is a Robotics PhD candidate at Georgia Tech and a graduate research assistant specializing in real-time perception, dynamics modeling, and trajectory optimization for hardware systems. With a decade of experience spanning academic research and industry, he focuses on propagating uncertainty to generate dynamic behavior from simple cost functions—a capability he applied on JPL’s DARPA RACER team for risk-aware motion planning. He pairs rigorous theory with hands-on embedded work, contributing to the AutoRally platform by enhancing Arduino-based chassis control and ESC communications. Based in Atlanta, Jason blends robotics research with practical software and IoT skills, producing computationally efficient algorithms that run on real systems. His background includes internships at GE Research and teaching experience in AI, reflecting both applied engineering and a commitment to mentorship. Notably, he repeatedly bridges the gap between uncertainty-aware planning and low-level hardware integration, enabling safer, faster robotic behaviors in the real world.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Robotics, Doctor of Philosophy - PhD, Robotics at Georgia Institute of Technology
Contributions:106 commits, 8 PRs, 93 pushes in 4 years
Contributions summary:Jason contributed to the autorally project by making significant changes to the Arduino-based chassis control code. Their work included adding a buzzer state machine and enhancing the chassis control system, likely for improved feedback and error handling. They also integrated functionality for querying and sending data from the Castle Serial Link, suggesting a focus on ESC (Electronic Speed Controller) interaction. Additionally, they removed comments and made adjustments to publish frequency, demonstrating a commitment to code optimization and refinement.
Contributions:72 commits, 73 pushes, 10 branches in 5 years 3 months
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