Max Robotics — Global Service Robot Directory + US Certification

About

Yingqi Li is a pioneering researcher at the intersection of soft continuum robotics and image-guided interventional systems, with a focus on safe, adaptive, and non-invasive medical technologies. Her key research areas include machine learning-based control of soft robots, MRI-guided focused ultrasound (FUS) navigation, and tensegrity-based compliant manipulators for human-robot collaboration. Li’s most cited work, “A Survey for Machine Learning-Based Control of Continuum Robots” (2021, 110 citations), provides a comprehensive roadmap for integrating reinforcement learning and adaptive control into soft robotic systems, addressing the critical challenge of accurate motion in minimally invasive surgery. She further advanced this field by developing a robotic platform for MRI-guided FUS (2021, 27 citations), enabling precise, non-invasive tumor treatment, and by proposing a reinforcement learning framework for adaptive continuous control of soft manipulators (2022, 27 citations), tackling the Sim2Real transfer problem. Her innovative tensegrity joint design (2023) offers a low-inertia, compact, and inherently compliant solution for safe human-robot interaction. With over 175 total citations and a trajectory of high-impact contributions, Li is shaping the future of intelligent, soft, and image-guided surgical robotics.