The field of autonomous aerial navigation is moving towards the development of more resilient and safe control systems. Researchers are exploring the combination of learning-based and safety controllers to improve the navigation of quadrotors in complex environments. The use of probabilistic risk assessment and reachability analysis is also becoming increasingly popular for designing risk-bounded controllers. Additionally, reinforcement learning with privileged information is being used to navigate around large obstacles. Noteworthy papers include: Improving the Resilience of Quadrotors in Underground Environments by Combining Learning-based and Safety Controllers, which proposes a combined controller that switches between learning-based and safety controllers. PRREACH: Probabilistic Risk Assessment Using Reachability for UAV Control, which presents a new approach for designing risk-bounded controllers using reachability analysis. Quadrotor Navigation using Reinforcement Learning with Privileged Information, which leverages privileged information to navigate around large obstacles.