Qipeng Gu

Chinese Academy of Sciences, Shandong Institute of Automation

Papers

Total Citations

H-Index

About

Qipeng Gu is a researcher specializing in robotic autonomous grasping, computer vision, and deep learning. His work focuses on enabling robots to perceive and interact with their environment through advanced visual perception and reinforcement learning techniques. Gu’s most impactful contribution is the development of the **Attention Grasping Network (AGN)**, a real-time, pixelwise grasp synthesis method that leverages a novel attention mechanism within a fully convolutional neural network. This innovation allows the system to automatically focus on salient features, significantly improving grasp detection accuracy and efficiency—a key step toward practical robotic manipulation. His 2021 paper on visual affordance detection, which employs an efficient attention convolutional neural network, has garnered 12 citations, highlighting its influence in the field. Gu also authored a comprehensive survey on robotic autonomous grasping, synthesizing progress in grasp detection, affordance detection, and model migration. With a total of over 25 citations across his most-cited works, Gu’s research is foundational for students and engineers aiming to bridge perception and action in robotics, offering both theoretical insights and real-time solutions for cluttered, unstructured environments.