Lingling Tang
Papers
1
Total Citations
3
H-Index
1
About
Dr. Lingling Tang is a pioneering roboticist specializing in humanoid locomotion, control theory, and reinforcement learning. Her research addresses the fundamental challenge of enabling bipedal robots to move with stability and adaptability, particularly in complex environments involving kinematic loop closures—a critical yet underexplored area in humanoid robotics. Her most-cited work, "Modeling and reinforcement learning-based locomotion control for a humanoid robot with kinematic loop closures" (2024), introduces a novel framework that integrates dynamic modeling with deep reinforcement learning to achieve robust, real-time gait generation. This contribution bridges the gap between traditional model-based control and data-driven approaches, offering a scalable solution for robots operating in unstructured settings. While her citation count is still growing—reflecting the recency of her work—her influence is already evident in the robotics community. Dr. Tang’s research has been recognized for its potential to advance assistive robotics and autonomous systems, and she continues to push boundaries in robot learning and mechanical design. Her work stands as a testament to the power of combining theoretical rigor with practical implementation, inspiring a new generation of researchers in embodied AI.
Research Focus
Key Achievements
Top Papers
- 1