Introduction

The development of flexible mobile manipulation robots is widely envisioned as a large breakthrough in technology and is expected to have a significant impact on our economy and society in the future. Mobile manipulation robots that are equipped with one or more gripper arms could fulfill various useful services in private homes such as cleaning, tidying up, or cooking, which would mean a significant time benefit to their owners. By supporting elderly and mobility-impaired people in the activities of daily living, such robots can reduce the dependency on external caregivers and support them to live a selfdetermined and autonomous life. Small and medium-sized enterprises would profit enormously from robotic co-workers that they can easily reconfigure to new production tasks. This technology would significantly lower the production costs of smaller companies and thus provide them with a significant competitive advantage. The goal of this book is to present novel approaches that enable mobile manipulation robots to be flexibly used in everyday life. The challenge in these applications is that robots operating in unstructured environments have to cope with less prior knowledge about themselves and their surroundings. Therefore, they need to be able to autonomously learn suitable probabilistic models from their own sensor data to robustly fulfill their tasks.