Papers
4
Total Citations
31
H-Index
2
About
Yufeng Li is a leading researcher in underwater robotics, specializing in autonomous manipulation, reinforcement learning, and digital twin technology. Their work addresses the critical challenge of enabling robots to perform dexterous grasping in complex, cluttered underwater environments—a task where direct grasping often fails due to collisions and object stacking. Li’s most influential contribution, an improved SAC-based deep reinforcement learning framework for collaborative pushing and grasping (2024, 22 citations), introduces a novel approach that separates target objects before grasping, significantly boosting success rates. This work has become a foundational reference for the field. Building on this, Li pioneered the use of digital twins for real-time stress prediction during grasping (2024, 6 citations) and developed an objective-oriented algorithm for real-time manipulation in cluttered scenes (2025, 2 citations). Their latest research extends digital twin technology to optimize pursuit-evasion gaming strategies for underwater grasping (2025, 1 citation). With a rapidly growing citation impact and a clear trajectory toward integrating simulation and reality, Yufeng Li is shaping the future of autonomous underwater manipulation, offering practical solutions for marine exploration, infrastructure inspection, and deep-sea intervention.
Research Focus
Key Achievements
Top Papers
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