Papers
3
Total Citations
18
H-Index
2
About
Xubo Yang is a pioneering researcher at the intersection of underwater robotics, digital twin technology, and reinforcement learning. His work focuses on enabling autonomous underwater robots to perform complex dynamic grasping tasks in challenging, unstructured environments. Yang’s major contributions include developing novel frameworks that integrate social learning with actor-critic algorithms, allowing robots to learn and adapt grasping strategies in real-time through digital twin simulations. His 2024 paper on social learning for dynamic grasping has garnered 11 citations, reflecting its immediate impact on the field. Additionally, his research on digital twin-based stress prediction for autonomous grasping (6 citations) and pursuit-evasion gaming strategy optimization (1 citation) demonstrates a systematic approach to enhancing robotic dexterity and decision-making under uncertainty. By bridging the gap between virtual simulations and physical underwater operations, Yang’s work significantly advances the safety and efficiency of autonomous underwater manipulation, with potential applications in deep-sea exploration, offshore infrastructure maintenance, and environmental monitoring.
Research Focus
Key Achievements
Top Papers
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