Risk Aware

Risk-aware systems aim to design autonomous agents, such as robots and self-driving vehicles, that make decisions while explicitly considering and mitigating potential risks, balancing efficiency with safety. Current research focuses on integrating risk measures (like Conditional Value-at-Risk) into planning algorithms (e.g., A*, RRT*, Model Predictive Control) and reinforcement learning frameworks, often employing deep learning models (e.g., transformers, Bayesian LSTMs) to handle uncertainty and complex environments. This field is crucial for deploying safe and reliable autonomous systems in real-world applications, impacting diverse areas from robotics and transportation to finance and healthcare.