Zeguo Li
Papers
2
Total Citations
4
H-Index
2
About
Zeguo Li is a robotics researcher whose work focuses on advancing the safety and control of compliant joint robots—a critical area for physical human-robot interaction (pHRI). His key research areas include robot control theory, neural network-based compensation, and sensor integration for elastic actuators. In his most-cited paper, "PD-type control with neural-network-based gravity compensation for compliant joint robots" (2015, 2 citations), Li pioneered a method to handle unknown or complex gravity models using neural networks, enabling more accurate and stable control without explicit modeling. His follow-up work, "Cascade control for compliant joint robots with redundant position sensors" (2016, 2 citations), addresses the challenge of achieving precise motion in robots with series elastic actuators—a design that improves shock absorption and safety during human interaction. By proposing a cascade control architecture that leverages redundant sensors, Li enhanced both tracking performance and robustness. Though his citation counts are modest, his contributions are foundational to the growing field of safe, human-friendly robotics, offering practical solutions for robots that must operate alongside people in everyday environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2