Leiyu Chen
Papers
1
Total Citations
3
H-Index
1
About
Leiyu Chen is a robotics researcher whose work focuses on intelligent grasping and manipulation, particularly the development of adaptive grasp strategies for multi-fingered robotic grippers. Their key research areas include computer vision, deep learning, and robotic control systems. Chen’s most notable contribution is the generation of grasp strategies using the DeepLab V3+ semantic segmentation model, specifically designed for three-finger grippers—a challenging area that bridges perception and action in robotics. This work, published in 2021, has garnered 3 citations and addresses a critical bottleneck in robotic dexterity: enabling robots to autonomously determine optimal grasping points based on visual input. By drawing on human grasping experience and integrating advanced neural network architectures, Chen’s research advances the practical deployment of robots in unstructured environments, such as manufacturing and service industries. Their work is particularly relevant for students and researchers interested in the intersection of deep learning and robotic manipulation, offering a data-driven pathway to more versatile and reliable robotic hands.
Research Focus
Key Achievements
Top Papers
- 1