Maoliang Yin
Papers
4
Total Citations
64
H-Index
3
About
Maoliang Yin is pioneering the next generation of intelligent robotic perception and motion planning, with research spanning sampling-based path planning, multi-modal object recognition, and zero-shot instance segmentation. His most impactful work, "Bi-AM-RRT*" (2023, 34 citations), introduces a fast, efficient motion planning algorithm for dynamic environments, addressing critical challenges in autonomous mobile robot navigation. Yin has also made significant strides in robot vision: his "Cross-Level Multi-Modal Features Learning With Transformer" (2023, 17 citations) advances RGB-D object recognition by fusing complementary visual and depth information, while his recent "ZISVFM" (2025, 11 citations) and "TransZSIS" (2026) tackle the pressing need for zero-shot instance segmentation in unstructured indoor settings, leveraging vision foundation models and transformer-based superpixel features to recognize novel objects without extensive annotated datasets. His work is directly enabling service robots to operate more autonomously and robustly in real-world environments. With a growing citation record and a clear trajectory toward solving fundamental perception and planning problems, Yin is establishing himself as a rising leader in robotic intelligence.
Research Focus
Key Achievements
Top Papers
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