Wenhui Huang

Wuhan University of Science and Technology

Papers

Total Citations

H-Index

About

Wenhui Huang is a robotics researcher specializing in autonomous manipulation, deep learning for perception, and dynamic modeling of robotic systems. Their work bridges computer vision and control, addressing critical challenges in how robots perceive and interact with complex environments. Huang’s most cited paper, “Robot Grasp with Multi-object Detection based on RGB-D Image” (2021, 10 citations), presents an integrated autonomous grasping system combining a depth camera, two-finger gripper, and six-degree-of-freedom arm. This work tackles the persistent problem of poor recognition and localization in cluttered scenes, advancing practical robotic pick-and-place capabilities. In a more recent contribution, “Dynamic Model Learning for Robotic Manipulators using BiLSTM Networks” (2022, 3 citations), Huang introduced a novel approach using bidirectional long short-term memory networks to learn manipulator dynamics. This method uniquely incorporates future state information, which prior research had overlooked, enabling more accurate inverse dynamic models. These contributions demonstrate Huang’s commitment to improving both the perceptual and physical intelligence of robots, with potential applications in manufacturing, logistics, and service robotics. Their work reflects a growing trend toward data-driven, learning-based approaches in classical robotics problems.