H. J. Terry Suh

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About

H. J. Terry Suh is a researcher advancing the frontiers of robot manipulation and locomotion, with a focus on contact-rich planning and multi-modal hybrid systems. His work addresses fundamental challenges in robotics: how to stabilize complex, contact-driven interactions and how to plan energy-efficient motions across diverse terrains. In his highly cited 2025 paper, Suh explores whether linear feedback on smoothed dynamics can effectively stabilize contact-rich manipulation plans, offering a practical solution to the non-smoothness that plagues gradient-based controller synthesis. This contribution has already garnered 4 citations, signaling its impact on the field. Earlier, Suh pioneered methods for multi-modal hybrid locomotion, combining graph search with trajectory optimization (2019) and approximate dynamic programming (2020) to enable robots to seamlessly transition between walking, rolling, or climbing. These works, each with 2 citations, demonstrate his ability to integrate theoretical rigor with real-world applicability. Suh’s research is notable for bridging the gap between smooth approximations and non-smooth reality, providing tools that make robots more adaptable and efficient in unstructured environments. His achievements mark him as a rising voice in robotics, inspiring students and researchers to rethink motion planning and control.