Yizhi Zhou

Yizhi Zhou

Ph.D. Student in Robotics, George Mason University

Talks and presentations

Consistent Cooperative Visual-Inertial Navigation based on Matrix Lie Group

July 09, 2025

Talk, State Estimation Section, ACC2025, Denver, CO, USA

In this talk, I introduce a consistent cooperative visual-inertial navigation framework based on Matrix Lie Group theory. The proposed approach enables multiple agents to achieve accurate and robust state estimation by fusing visual and inertial measurements in a cooperative manner. I discuss the theoretical foundations, algorithmic design, and experimental validation on real-world robotic platforms, highlighting improvements in consistency and scalability for multi-agent navigation.

D3G: Learning multi-robot coordination from demonstrations

October 03, 2023

Talk, Imitation Learning Section, IROS2023, Detroit, MI, USA

In this talk, I present D3G, a novel framework for learning multi-robot coordination from demonstrations. The approach leverages imitation learning to enable groups of robots to perform complex collaborative tasks efficiently and robustly. I discuss the key challenges in multi-robot coordination, our proposed solution, experimental results on real-world robotic platforms, and future directions for scalable autonomous systems.