Robustness techniques are methods used to enhance the reliability and stability of systems, particularly in the fields of engineering and computer science. These techniques aim to ensure that a system can perform well under varying conditions and resist failures caused by unexpected inputs or environmental changes. Common approaches include error detection, redundancy, and adaptive algorithms. In machine learning, robustness techniques help models maintain performance despite noisy data or adversarial attacks. Strategies such as data augmentation, regularization, and ensemble methods are employed to improve a model's ability to generalize and withstand perturbations, ensuring that it remains effective in real-world applications.