Yiling Li
Papers
1
Total Citations
1
H-Index
1
About
Yiling Li is a robotics researcher whose work centers on robotic manipulation, computer vision, and multi-modal perception. Her most-cited paper, "A Method Based on Multi-Modal Fusion of RGB-D Images for Detecting the Grasping Pose of a Robotic Arm" (2024), tackles a critical bottleneck in autonomous robotics: achieving precise, real-time grasp detection in cluttered environments. By fusing RGB and depth data, Li’s method addresses the dual challenges of imprecise grasp-area representation and insufficient pose accuracy, offering a more robust solution for complex settings. Though early in its citation trajectory, this work signals a significant contribution to the field, with potential applications in industrial automation and service robotics. Li’s research is particularly notable for its focus on bridging the gap between sensor data and actionable robotic commands, a key step toward more adaptive and reliable robotic systems. Her approach exemplifies the growing importance of multi-modal fusion in overcoming the limitations of single-sensor perception, making her a promising voice in the next generation of robotics innovation.
Research Focus
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Top Papers
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