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Autonomous systems operating in uncertain environments without human intervention must consider several factors, including safety, reliability, and task success. State-of-the-art methods have made progress in addressing these factors individually, but often fail to unify them for deployment in real-world systems. My dissertation aims to combine methods in planning under uncertainty, failure recovery, and explainability, providing a holistic framework for comprehensive safe autonomy in real-world deployment.