Tingguang Li
Papers
20
Total Citations
497
H-Index
10
About
Tingguang Li is a leading researcher in autonomous robotics, with a focus on intelligent navigation, manipulation, and locomotion. His work spans deep reinforcement learning, path planning, and perception, driving advances in how robots explore, interact with, and move through complex environments. He introduced the Deep Reinforcement Learning Supervised Autonomous Exploration framework (108 citations), which revolutionized long-term planning for robot exploration, and developed GMR-RRT* (103 citations), a sampling-based path planner that uses Gaussian Mixture Regression to dramatically improve planning efficiency. Li also created HouseExpo (62 citations), a large-scale 2D indoor layout dataset that has become a benchmark for learning-based mobile robot algorithms, addressing the critical need for standardized experimental platforms. His recent work on quadrupedal robots, combining reinforcement learning with generative pre-trained models to achieve lifelike agility and play (48 citations), showcases his ability to push the boundaries of robotic dexterity and adaptability. With additional contributions to in-hand manipulation, elevator button recognition, and terrain-adaptive locomotion, Li’s research consistently bridges theory and real-world application, earning him over 400 total citations and establishing him as a key innovator in autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression103 citations · 2022
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- 5Learning Hierarchical Control for Robust In-Hand Manipulation34 citations · 2020
- 6A Novel OCR-RCNN for Elevator Button Recognition30 citations · 2018
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