Control Barrier

Control barrier functions (CBFs) are mathematical tools ensuring the safety of dynamical systems, primarily robots, by guaranteeing that the system's state remains within a safe region. Current research focuses on learning CBFs from data, often using neural networks, to overcome the challenges of manually designing them, particularly in complex or uncertain environments. This allows for safer and more efficient robot control in applications like autonomous navigation and human-robot interaction, improving the reliability and trustworthiness of these systems. The development of efficient algorithms for learning and verifying CBFs is a key area of ongoing investigation.