Safety Assurance

Safety assurance for increasingly complex autonomous systems, particularly those employing machine learning, focuses on mitigating risks stemming from unpredictable behavior, adversarial attacks, and model uncertainty. Current research emphasizes developing robust safety architectures, including input-output filters, safety agents, and hierarchical systems, alongside novel algorithms like Optimistically Safe Online Convex Optimization for handling constraints and uncertainty in dynamic environments. These advancements are crucial for ensuring the reliable and safe deployment of AI agents across various sectors, from autonomous vehicles to industrial robotics, ultimately fostering trust and wider adoption of these technologies.