Yaonao Wang
Papers
1
Total Citations
2
H-Index
1
About
Yaonao Wang is a researcher specializing in robotics, artificial intelligence, and autonomous decision-making under uncertainty. Their work focuses on developing computationally efficient planning algorithms for systems operating with partial observability—a critical challenge in real-world robotics. Wang’s most notable contribution, "High-efficiency online planning using composite bounds search under partial observation" (2022), introduces a novel approach that combines multiple bounds to accelerate search-based planning, enabling faster and more reliable decision-making in complex, dynamic environments. While this paper has garnered 2 citations to date, its significance lies in its potential to advance autonomous navigation, manipulation, and exploration tasks where real-time performance is essential. Wang’s research bridges theoretical rigor and practical deployment, offering scalable solutions for robots that must act with incomplete information. Their work is particularly relevant for students and researchers in robotics, reinforcement learning, and probabilistic planning, providing a foundation for future innovations in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1