Reliable Plan Selection with Quantified Risk-Sensitivity

Robots in many domains need to plan and make decisions under uncertainty; for example, autonomous underwater vehicles (AUVs) gathering data in environments inaccessible to humans, need to perform automated task planning. Planning problems are typically solved by risk-neutral optimization maximizing a single objective, such as limited time or energy consumption. A typical probabilistic planner synthesizes a plan to reach the desired goals with a maximum expected reward, given the possible initial states and actions of the world. In this work, we additionally consider risk metrics for selecting solutions to such planning problems. Consider a marine robotics mission scenario where the task is to survey pipeline segments safely based on various risk measurements.