Haiying Wan
Papers
2
Total Citations
17
H-Index
2
About
Haiying Wan is a rising researcher at the intersection of control theory and intelligent robotics, with key contributions in model-free learning-based control and modular robotic design. Her work on integrated learning self-triggered control for continuous-time systems, published in 2023, established a novel framework that guarantees convergence without requiring a system model—a significant advance for adaptive automation. This paper has already garnered 9 citations, reflecting its early impact on the control community. In 2024, Wan introduced a groundbreaking method for configuring modular robotic arms using Double Deep Q-Networks with Prioritized Experience Replay. This approach intelligently selects module combinations to achieve desired performance, geometric symmetry, and uniform mass symmetry—critical for versatile, reconfigurable robots. The work has earned 8 citations and demonstrates her ability to bridge deep reinforcement learning with practical hardware design. Wan’s research is notable for its dual focus on theoretical guarantees and real-world applicability, positioning her as a promising voice in next-generation autonomous systems and modular robotics.
Research Focus
Key Achievements
Top Papers
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