Papers
26
Total Citations
242
H-Index
9
About
Honglei An is a robotics researcher whose work sits at the intersection of legged robot locomotion, actuator control, and intelligent motion planning. His research has made significant contributions to quadruped robot systems, focusing on three core areas: contact force estimation, model predictive control (MPC), and electro-hydraulic actuator dynamics. Among his most influential contributions is a sensor-free contact force estimation method for legged robots that enables impedance control without relying on expensive force sensors — a practical breakthrough cited 52 times that broadens the accessibility of high-performance legged systems. His work on multi-scale online estimation for electro-hydraulic actuators addresses real-world challenges including time-varying parameters and measurement noise, reflecting a deep commitment to robust, deployable systems. An has also pioneered hybrid control approaches that merge the predictive power of MPC with reinforcement learning, advancing autonomous locomotion on complex terrain. Beyond quadruped robotics, his research extends into rehabilitation technology, including adaptive admittance control for robotic knee prostheses and body weight support exoskeletons, demonstrating the breadth of his impact across both industrial and assistive robotics. With a growing citation record spanning locomotion optimization, gait design, and adaptive control, Honglei An represents a versatile and impactful voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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- 3Model Predictive Control of Quadruped Robot Based on Reinforcement Learning20 citations · 2022
- 4Quadruped Robot Control through Model Predictive Control with PD Compensator16 citations · 2021
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- 8Adaptive Walking on Slope of Quadruped Robot Based on CPG9 citations · 2019
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