Legged System

Legged robotic systems research focuses on developing robots capable of robust and efficient locomotion across challenging terrains, surpassing the limitations of wheeled robots. Current research emphasizes gait optimization using methods like mixed distribution cross-entropy and nonlinear model predictive control, often coupled with reinforcement learning to enhance agility and adaptability in diverse environments. This work is significant for advancing both fundamental understanding of legged locomotion and enabling practical applications in areas such as planetary exploration, search and rescue, and hazardous environment navigation. The development of efficient, optimization-free control strategies is a key area of ongoing investigation.