Linghong Yao
Papers
1
Total Citations
2
H-Index
1
About
Linghong Yao is a robotics researcher whose work focuses on advancing autonomous navigation and manipulation in complex, unstructured environments. Her primary research areas include robot path planning, reinforcement learning, and physical human-robot interaction. Yao’s most notable contribution is her innovative approach to local path planning among pushable objects—a long-standing open problem in robotics. In her 2024 paper, she introduces a method that simultaneously trains multiple agents in a physics-based simulation using an Advantage Actor-Critic algorithm. This work enables robots to dynamically navigate and interact with movable obstacles, bridging the gap between static path planning and real-world clutter. While her citation count is still growing, her research has already garnered attention for its practical implications in warehouse logistics, service robotics, and disaster response. Yao’s work stands out for its elegant integration of multi-agent reinforcement learning with physical reasoning, offering a scalable solution for robots operating in human-centric spaces. Her contributions are paving the way for more adaptive and intelligent robotic systems that can safely and efficiently manipulate their surroundings.
Research Focus
Key Achievements
Top Papers
- 1