Aohua Liu

Southeast University, Harbin Institute of Technology

Papers

Total Citations

H-Index

About

Aohua Liu is a robotics researcher whose work sits at the intersection of manipulation, learning, and control. Their most notable contribution is in the domain of robotic cable-in-duct installation, where they pioneered a visual–tactile learning framework that enables robots to handle flexible, deformable objects—a notoriously difficult task in automation. This work, published in 2024, has already garnered 11 citations, reflecting its immediate relevance to industrial and service robotics. Liu also addresses fundamental challenges in robotic manipulation through reinforcement learning, developing a compound controller for uncertain manipulator trajectory tracking that fuses traditional control laws with deep RL to improve both accuracy and adaptability. This 2022 paper, with 5 citations, lays important groundwork for robust, uncertainty-tolerant robotic systems. By bridging model-based and learning-based approaches, Liu’s research offers practical pathways for deploying robots in complex, real-world environments where precision and adaptability are critical. Their work is particularly valuable for students and researchers interested in the intersection of control theory, machine learning, and physical interaction.