Service robots require interactive programming interfaces that allow users without programming experience to easily instruct the robots. Systems following the programming by demonstration (PbD) paradigm that were developed within the last years are getting closer to this goal. However, most of these systems lack the possibility for the user to supervise and alter the course of program generation after the initial demonstration was performed. In this paper we present an approach, where the user is able to supervise the entire program generation process and to annotate, and edit system hypotheses. Moreover, the knowledge representation and algorithms presented enable the user to generalize the generated program by annotating conditions and object selection criteria via a 3D simulation and graphical user interface. The resulting PbD-system widens the PbD approach in robotics to the interactive generation of flexible robot programs based on demonstration and annotations.

While distributed control architectures have many advantages over centralized ones, such as their inherent modularity and fault tolerance, a major problem of such architectures is to ensure the goal-oriented behaviour of the controlled system. This paper presents a framework within which the coordination skills required for goal-orientedness are learned from user demonstrations. The framework is based on a state-space model of the single agents building the system and a corresponding model of the coordination mechanism. Our mobile robot PRIAMOS provides an application example.

The problem of adapting mobile robot navigation to changes in the environment is usually approached by modifying an internal world model. Descriptions on different levels of abstraction provide the information necessary for navigation and therefore influence the robot's behaviour. The effect of such indirect adaptation is limited. The approach presented in this paper describes a new technique for direct integration of navigation experience in path planning. Thus, not only the world knowledge, but also the planning behaviour is improved over time. Experiments are carried out on a robot which is controlled by a layered architecture. It is integrated in a multirobot control environment which is described. The focus of the article is towards improving the higher navigation levels. The main idea being presented is the realization of adaptive behaviour not only on the level of reflexes, but also with respect to the planning capabilities of the robot. The application of learning techniques allows to continuously improve the estimation of plan costs and therefore the inherent strategy of the topological planner. It is illustrated that a combined learning of world description and navigation allows fast and sophisticated reaction to new environmental conditions.>