In Soo Ahn

Papers

Total Citations

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About

In Soo Ahn’s research lies at the intersection of human–robot interaction and multi-robot coordination, with a strong emphasis on practical, real-world implementation. Her work advances two core areas: intuitive control interfaces for assistive robotics and distributed cooperative control for autonomous robot teams. Ahn’s most cited paper (10 citations) introduces an EMG-based hand gesture control system, where a wearable MyoWave muscle sensor enables users to command an in-home assistance robot through natural muscle signals—a key step toward accessible, non-invasive human-machine interfaces. In parallel, her studies on distributed control (6 citations each) tackle the challenge of enabling multiple mobile robots to cooperate under limited sensing and communication, using feedback linearization for formation control and vision-based algorithms for target tracking. These experiments demonstrate robust coordination even with kinematic constraints, bridging theory and practice. Ahn’s work is notable for its hands-on validation: she builds and tests real robotic systems, from wearable sensors to multi-robot teams, ensuring her contributions are immediately applicable. Her research offers a compelling vision of robots that work seamlessly with humans and each other.