Papers
2
Total Citations
5
H-Index
2
About
Haolin Jiang is a rising researcher in the field of humanoid robotics, with a primary focus on bipedal locomotion, gait optimization, and whole-body control. His work addresses the fundamental challenge of achieving stable, efficient walking in underactuated humanoid platforms. Jiang’s major contributions include the development of a zeroing neural networks (ZNN)-based gait optimization strategy that converts the angular momentum linear inverted pendulum (ALIP) problem into a time-varying quadratic programming (TVQP) framework, enabling adaptive real-time adjustments. He also proposed an improved hierarchical optimization framework that integrates model predictive control (MPC) with a whole-body planner and controller, specifically designed for lightweight robots lacking ankle roll degrees of freedom. Although early in his career, his 2025 papers have already garnered citations, signaling growing interest in his approaches. Jiang’s work is notable for bridging advanced optimization theory with practical robotic control, offering scalable solutions for underactuated systems. His research holds significant promise for the next generation of agile, human-like robots capable of navigating complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
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