Yuheng Liu

Changchun University of Science and Technology

Papers

Total Citations

H-Index

About

Yuheng Liu is a robotics researcher whose work focuses on the intersection of computer vision and robotic manipulation, particularly in unstructured environments. His key contributions center on developing efficient, lightweight detection methods for robotic grasping, addressing the challenge of enabling robots to accurately compute grasp postures for irregularly shaped objects in cluttered scenes. Liu’s most cited paper, “Detection method of robot grasp based on lightweight network” (2021), proposes a streamlined neural network approach to improve real-time grasp detection, tackling the difficulty of calculating suction cup grasping poses when objects are placed unpredictably. While his citation count is currently modest, with 2 citations for this work, his research holds practical significance for advancing robotic automation in manufacturing and logistics. Liu’s work is notable for its emphasis on computational efficiency, making it suitable for deployment on resource-constrained robotic systems. His contributions are particularly relevant for students and researchers interested in practical robotics, deep learning for perception, and the ongoing challenge of bridging the gap between controlled lab environments and real-world, unstructured scenarios.