Safe Locomotion

Safe locomotion in robots focuses on developing control strategies that enable robots to move reliably and safely in complex and unpredictable environments. Current research emphasizes integrating model-predictive control (MPC) with machine learning techniques, such as reinforcement learning and Koopman operators, to improve robustness and predictability while incorporating control barrier functions (CBFs) for collision avoidance and safety guarantees. This work is crucial for expanding the capabilities of legged and wheeled robots in various applications, including search and rescue, industrial automation, and space exploration, by enabling them to navigate challenging terrains and interact safely with their surroundings.