Papers
4
Total Citations
18
H-Index
3
About
Xiaogang Li is a pioneering robotics researcher whose work bridges the critical gap between intelligent perception, adaptive control, and soft actuation. His research focuses on three key areas: disturbance compensation for dexterous manipulation, reinforcement learning for autonomous navigation, and neuromorphic tactile sensing. Li’s major contributions include developing a disturbance observer-based control system that compensates for friction torque in tendon-sheath-driven humanoid hands, significantly improving operational accuracy for delicate tasks. He also proposed the Proximal policy-Dijkstra (PP-D) algorithm, which combines reinforcement learning with classical pathfinding to enable efficient real-time navigation in complex warehouse layouts. In sensing, Li introduced a triboelectric artificial synapse that mimics human tactile perception for material identification, advancing adaptive learning in robotics. His work on eco-friendly ionic soft actuators, using bacterial cellulose and ionic liquids, demonstrates his commitment to sustainable soft robotics. With over 18 citations across his most-cited papers—including recent 2025 publications—Li’s research is gaining rapid recognition for its practical impact on humanoid robotics, warehouse automation, and intelligent perception systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4