Qixin Sha
Papers
3
Total Citations
23
H-Index
2
About
Qixin Sha is a researcher at the forefront of autonomous underwater vehicle (AUV) technology and reinforcement learning, with a focus on bridging the gap between simulation and real-world deployment. His work centers on developing high-fidelity simulation systems and efficient control strategies to enhance AUV autonomy and operational safety. Sha’s most cited paper, “Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine” (2023, 14 citations), introduces a comprehensive simulation platform that leverages the Mission Oriented Operating Suite (MOOS) and Unreal Engine 4 to reduce development costs and risks. He further advances sim-to-real transfer learning in “Shaping Progressive Net of Reinforcement Learning for Policy Transfer with Human Evaluative Feedback” (2021, 8 citations), addressing sampling efficiency and safety challenges in robot control. His recent work, “Task-Sequencing Optimization Using DSSA Algorithm for AUV with Limited Endurance” (2025), tackles mission efficiency for energy-constrained AUVs. Sha’s contributions are pivotal for enabling robust, cost-effective AUV operations, with his simulation and learning frameworks laying the groundwork for next-generation underwater robotics.
Research Focus
Key Achievements
Top Papers
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