Nonlinear Robotic System

Nonlinear robotic systems research focuses on developing control strategies and models for robots exhibiting complex, non-linear behaviors. Current efforts concentrate on improving model accuracy and efficiency through techniques like data-driven methods (e.g., Koopman operator-based approaches, Gaussian Processes), optimal experiment design for efficient exploration, and robust control strategies that account for uncertainties and disturbances. These advancements are crucial for enhancing robot performance in challenging real-world scenarios, leading to safer and more adaptable robots across various applications, including legged locomotion and autonomous navigation.