Xiangbo Lin
Papers
8
Total Citations
125
H-Index
5
About
Xiangbo Lin is a leading researcher in the field of robotic dexterous manipulation, with a primary focus on enabling multi-fingered robotic hands to achieve human-like, functional grasping. His work bridges the gap between stable grasp generation and task-oriented manipulation, a critical challenge in intelligent robotics. Lin’s most influential contribution is the development of a grasp synthesis framework that leverages semantic representations of object-hand interactions, allowing robots to understand not just *how* to hold an object, but *why*—enabling functional, post-grasp manipulation. His seminal 2021 paper, “Toward Human-Like Grasp,” has garnered 50 citations, establishing a foundational approach in the field. To support this research, Lin created the DexFuncGrasp dataset (2024), a cost-effective real-simulation annotation system for dexterous functional grasps. His recent work integrates visuo-tactile fusion via multi-agent deep reinforcement learning and adaptive motion planning with force feedback, pushing toward closed-loop, contact-rich manipulation. With over 120 total citations across his publications, Lin’s research is shaping the next generation of dexterous robotic hands capable of performing precise, human-like tasks in dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 5Hand-object information embedded dexterous grasping generation7 citations · 2023
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- 8GECM: graph embedded convolution model for hand mesh reconstruction2 citations · 2022