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About

Keiko Nagami is a robotics researcher pushing the boundaries of autonomous aerial systems and real-time navigation. Her work centers on three key areas: aerial manipulation, safe robot navigation in novel 3D scene representations, and simulation for autonomous drone racing. Nagami’s major contributions include introducing the concept of a “velocity sufficiency region” for aerial grasping, enabling quadrotors to capture moving targets mid-flight—a problem with significant implications for search-and-rescue and package delivery. She also pioneered Splat-Nav, a real-time navigation pipeline that leverages Gaussian Splatting (GSplat) maps for safe planning and robust localization, bridging the gap between cutting-edge computer vision and practical robotics. Her work on AirSim Drone Racing Lab has become a standard simulation framework for prototyping autonomous racing algorithms, while her research on epistemic uncertainty in learning-based perception systems addresses a critical challenge in deploying neural networks on robots. With over 75 citations across her most-cited papers, Nagami’s contributions have been recognized at top venues like ICRA and IEEE conferences. Her NerfBridge work, which brings real-time Neural Radiance Field training to robotics, further cements her role as a leader in integrating implicit scene representations into autonomous systems.