Yueqin Gu

Papers

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About

Yueqin Gu is a researcher in soft robotics, focusing on the control and motion evolution of flexible, cable-driven robotic systems. Her work addresses a fundamental challenge in the field: the difficulty of modeling and controlling soft robots due to their compliant materials, which render traditional rigid-robot methods ineffective. Gu’s most cited paper, "Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning" (2020, 36 citations), introduces a data-driven approach that combines deep reinforcement learning with modeling to achieve precise position control of a soft arm—a significant step toward practical, autonomous soft robots. Her earlier research, "Research on Motion Evolution of Soft Robot Based on VoxCAD" (2019, 6 citations), explores how soft robots can evolve their motion patterns using simulation tools, laying groundwork for adaptive locomotion. By bridging machine learning and soft robotics, Gu’s contributions offer scalable solutions for applications in medical devices, search-and-rescue, and human-robot interaction, where safety and flexibility are paramount. Her work is a valuable resource for students and researchers seeking to understand the intersection of reinforcement learning and compliant robotic design.