Huiliang Jin

Technion – Israel Institute of Technology, Shanghai Jiao Tong University

Papers

Total Citations

H-Index

About

Huiliang Jin has carved a distinctive niche at the intersection of nonlinear dynamics, oscillatory neural control, and robotic manipulation, with a focused expertise on the seemingly playful yet profoundly complex task of robotic yo-yo playing. His foundational work introduces a dynamic two-degree-of-freedom model for the yo-yo, capturing its unilateral constraint and splitting its motion into four distinct phases, including a novel restitution effect for collisions. Jin’s major contribution lies in applying coupled oscillatory neural networks to achieve closed-loop control of open-loop unstable rhythmic tasks. He demonstrated that the inherent phase-locking property of these oscillators could stabilize the yo-yo’s periodic motion, a breakthrough for tasks requiring precise temporal coordination. His most cited paper (52 citations) on oscillatory neural networks for yo-yo control established a paradigm for using biological-inspired circuits in robotic manipulation. Later, he advanced the field with return map parameterization and cycle-wise planning, enabling nominal control generation through optimization. With a cumulative citation count approaching 100, Jin’s work remains a cornerstone for researchers exploring neural control of rhythmic tasks and underactuated systems, proving that even a child’s toy can inspire rigorous, impactful engineering.