Dianhao Chen
Papers
1
Total Citations
28
H-Index
1
About
Dianhao Chen is a robotics researcher whose work sits at the intersection of control theory, nonlinear dynamics, and legged locomotion. His primary research areas include model predictive control (MPC), geometric mechanics, and the application of Lie group theory to robotic systems. Chen’s most notable contribution is the development of an error-state MPC framework on connected matrix Lie groups, which provides a principled way to linearize tracking error dynamics directly in the Lie algebra. This approach, detailed in his highly cited 2022 paper (28 citations), enables more stable and geometrically consistent control for complex robotic platforms like legged robots. By deriving linearized equations of motion that respect the underlying manifold structure of robot configurations, Chen’s work bridges a critical gap between abstract geometric control theory and practical real-time control implementation. His research has significant implications for improving the robustness and agility of walking, running, and jumping robots, particularly in unstructured environments. Chen’s contributions are helping to shape a new generation of control architectures that are both mathematically elegant and computationally tractable for deployment on physical hardware.
Research Focus
Key Achievements
Top Papers
- 1