This book investigates the problem of competently accomplishing everyday manipulation activities for autonomous robots as a plan-based control problem. We propose TRANER (TRAnsformational PlanNER) as a plan transformation system for the optimization of everyday activities. TRANER advances the state-of-the-art in three important ways. First, it extends concurrent reactive plan representation to support the specification of robust and transformable plans. Second, it proposes a library of general and flexible plans for a household robot. Third, it provides a suite of general transformation rules that are capable of improving the course of activities in important and novel ways. Comprehensive and extensive experiments in a realistic simulation environment show that transformational planning enables robots to substantially improve their performance by tailoring their course of activity to their tasks, environments, and capabilities.