Yunpeng Mei
Papers
3
Total Citations
8
H-Index
2
About
Yunpeng Mei is a robotics and computer vision researcher focused on advancing robotic manipulation and perception. His work centers on instance-level robotic grasping, world models for manipulation, and 6-DoF object pose tracking—critical areas for intelligent manufacturing and autonomous systems. In his highly cited paper "RoG-SAM: A Language-Driven Framework for Instance-Level Robotic Grasping Detection" (2025, 3 citations), Mei introduces a novel approach that leverages language-driven segmentation to enable flexible, category-agnostic grasping, overcoming the limitations of predefined instance constraints. This work, alongside "Improving world models for robot arm grasping with backward dynamics prediction" (2024, 3 citations), demonstrates his commitment to enhancing robot adaptability through predictive modeling. Additionally, his research on "Multi-modal 6-DoF object pose tracking: integrating spatial cues with monocular RGB imagery" (2024, 2 citations) advances object tracking by fusing spatial and visual data for precise pose estimation. Despite being early in his career, Mei's publications already show significant impact, with each garnering citations that underscore their relevance to ongoing challenges in robotics. His contributions promise to bridge language understanding and physical manipulation, paving the way for more intelligent and flexible robotic systems.
Research Focus
Key Achievements
Top Papers
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