Jiaqi Suo
Papers
3
Total Citations
10
H-Index
2
About
Jiaqi Suo is a rising researcher at the intersection of soft robotics, wireless control systems, and deep learning-driven shape morphing. Their work addresses fundamental challenges in creating miniaturized, programmable robotic systems with fewer physical constraints. Suo’s early research introduced a novel approach to wireless multiplexing control using magnetic coupling resonance, enabling independent control of multiple actuators in compact robots without cumbersome wiring—a contribution cited 6 times for its potential to streamline robotic design. More recently, Suo has pioneered the application of deep learning on point cloud data to achieve universal 3D shape morphing in soft robotic devices. Their 2024 papers (each garnering 2 citations) demonstrate how neural networks can learn to mimic and inversely control complex, programmable deformations in array-based actuators. This work bridges machine learning and soft robotics, offering a powerful tool for human-machine interfaces, biomimetic systems, and biological interaction tools. By combining wireless control innovation with AI-driven morphology, Jiaqi Suo is shaping the future of adaptive, untethered robotic systems.
Research Focus
Key Achievements
Top Papers
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