Papers
1
Total Citations
2
H-Index
1
About
Xiangkai Li is a researcher at the forefront of robotic manipulation and computer vision, with a primary focus on advancing grasping detection for autonomous systems. His most notable contribution is the development of EAGA-Net, a novel simulation-based grasping detection dataset and network that introduces efficient adaptability of gripper attributes. This work, published in 2025, addresses a critical challenge in robotics: enabling grippers to generalize across diverse object geometries and grasping scenarios. By leveraging synthetic data and a tailored neural architecture, Li’s approach enhances the robustness and flexibility of robotic grasping systems, a cornerstone for applications in manufacturing, logistics, and service robotics. With 2 citations already in its early publication stage, EAGA-Net signals growing recognition of its potential impact. Li’s research bridges simulation and real-world deployment, offering scalable solutions that reduce the need for extensive physical data collection. His work stands out for its practical emphasis on gripper adaptability, a key bottleneck in modern robotics, and positions him as an emerging voice in the intersection of deep learning and robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1