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About

Raman Goyal is a pioneering roboticist whose work sits at the intersection of soft robotics, control theory, and structural dynamics. His primary research focuses on the modeling and control of tensegrity robots—lightweight, compliant structures composed of bars in compression and cables in tension. Goyal’s major contributions include developing both model-based and data-driven control strategies for these complex systems, enabling precise shape regulation and end-effector positioning. His 2020 paper on "Model and Data Based Approaches to the Control of Tensegrity Robots" (28 citations) provides foundational methods for regulating position, velocity, and acceleration in soft-robotic applications. In a particularly innovative vein, his work on "Gyroscopic Tensegrity Robots" (16 citations) introduces controllable spinning wheels to enhance mobility and manipulation, opening new possibilities for dynamic structural control. Goyal has also advanced data-based control for partially-observed robotic systems, developing the POD2C framework that extends iterative LQR to high-dimensional robots with limited sensor feedback. His research, spanning visual feedback control and autoregressive modeling, has accumulated over 60 citations and is shaping the future of adaptable, resilient robotic structures.