Safe Autonomous Decision

Safe autonomous decision-making focuses on developing systems that can make reliable and ethical choices in dynamic, unpredictable environments while guaranteeing safety. Current research emphasizes robust control algorithms, such as chance-constrained control and reinforcement learning (including physics-guided and reach-avoid variants), often integrated with foundation models like large language models for improved perception and decision-making. These advancements are crucial for deploying autonomous systems in various sectors, including robotics, autonomous vehicles, and aerospace, where safety and reliability are paramount. The field is also actively addressing challenges related to fairness, explainability, and safe testing methodologies for these systems.