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.

Accurate system modeling is crucial for safe, effective control, as misidentification can lead to accumulated errors, especially under partial observability. We address this problem by formulating informative input design and model identification adaptive control (MIAC) as belief space planning problems, modeled as partially observable Markov decision processes with belief-dependent rewards (ρ-POMDPs). We treat system parameters as hidden state variables that must be localized while simultaneously controlling the system. We solve this problem with an adapted belief-space iterative Linear Quadratic Regulator (BiLQR). We demonstrate it on fully and partially observable tasks for cart-pole and steady aircraft flight domains. Our method outperforms baselines such as regression, filtering, and local optimal control methods, even under instantaneous disturbances to system parameters.