Aijia Annie Yang

Hello,

I am an undergraduate student at University of California, San Diego studying Mechanical Engineering (Specialization in Controls and Robotics) and double major in Mathematics Applied Science with a focus on Computer Science.

My academic and professional interests center on robotics, software engineering, and AI/ML. I am passionate about innovation, creativity, and exploring unconventional approaches in STEM, with a drive to push boundaries and develop impactful solutions. With multiple years of experience in CAD modeling and programming, I have built a strong foundation in both hardware and software, enabling me to approach problems from an interdisciplinary perspective.

annieyaj [at] outlook [dot] com  /  CV  /  Linkedin  /  Github

This paper introduces MADR (MPC-guided Adversarial DeepReach), a framework that improves the learning of differential game value functions for robust control. By integrating active MPC-based supervision into self-supervised Hamilton-Jacobi reachability learning, MADR efficiently approximates two-player, zero-sum differential games and yields optimal strategies for both agents. The method demonstrates strong performance across simulated and real robotic platforms, including high-dimensional systems such as turtlebots, drones, and humanoids.

[paper] [website] [video]

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Last updated: Nov 3, 2025