Disturbance Injection

Disturbance injection is a technique used to enhance the robustness and reliability of various systems, particularly in robotics and autonomous driving, by intentionally introducing controlled perturbations during training or operation. Current research focuses on developing algorithms that leverage disturbance injection within imitation learning frameworks, often incorporating Bayesian methods or H-infinity constraints to optimize policy learning and ensure stability. This approach is proving valuable for improving the performance and safety of autonomous systems in complex and unpredictable environments, leading to more reliable and adaptable robots and vehicles.