Papers
5
Total Citations
247
H-Index
4
About
Katherine L. Bouman is a pioneering researcher at the intersection of computer vision, robotics, and safety-critical control. Her work is defined by two major thrusts: inferring physical properties from visual data, and ensuring safe autonomy under uncertainty. In her highly cited work on *visual vibrometry* (2015, 122 citations), Bouman pioneered a method to estimate material properties—such as stiffness and damping—from tiny, often imperceptible motions in video. By connecting vibration mechanics with computer vision, she opened new avenues for scene understanding with applications in structural engineering and robotics. Building on this, Bouman has made significant contributions to safe robotic control. She developed *measurement-robust control barrier functions* (2021), a rigorous framework that guarantees safety even when a robot’s state estimates are imperfect. Her more recent work on *self-supervised online learning* (2022) extends this to real-world vision-based systems, enabling robots to autonomously characterize their own measurement errors during operation. Through these contributions, Bouman has established herself as a leading voice in creating robots that are both perceptive and provably safe.
Research Focus
Key Achievements
Top Papers
- 1Visual vibrometry: Estimating material properties from small motions in video122 citations · 2015
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