In order to be able to replace a human operator, a robotic manipulation system needs to deal with a variety of possible actions. These actions may be more or less constrained in their motion profile and in the accuracy of the transport goals. The robotic system can make use of some of this variation to simplify the control to improve the efficiency of the generated motion. Nevertheless, the human's intention behind the manipulation may not change. We introduce the Elastic Power Path to optimize paths with respect to efficient control in the context of abstractly represented tasks. Our experiments show, that the proposed Elastic Power Path is an efficient method to achieve this aim. The magnitude and the number of turnarounds of the accelerations along the path are significantly reduced.

Robots are meanwhile able to perform several tasks. But what happens, if one or multiple of the robot's joints fail? Is the robot still able to perform the required tasks? Which capabilities of the robot get limited and which ones are lost? We propose an analysis of manipulator structures for the comparison of a robot's capabilities with respect to efficient control. The comparison is processed (1) within a robot in the case of joint failures and (2) between robots with or without joint failures. It is important, that the analysis can be processed independently of the structure of the manipulator. The results have to be comparable between different manipulator structures. Therefore, an abstract representation of the robot's dynamic capabilities is necessary. We introduce the Maneuverability Volume and the Spinning Pencil for this purpose. The Maneuverability Volume shows, how efficiently the end-effector can be moved to any other position. The Spinning Pencil reflects the robot's capability to change its end-effector orientation efficiently. Our experiments show not only the different capabilities of two manipulator structures, but also the change of the capabilities if one or multiple joints fail.

We propose a framework for the sensor-based estimation of manipulation-relevant object properties and the abstraction of known actions in a learning setup from the observation of humans. The descriptors consists of an object-centric representation of manipulation constraints and a scene-specific action graph. The graph spans between the typical places, where objects are placed. This framework allows to abstract the strongly varying actions of a human operator and to monitor unexpected new actions, that require a modification of the knowledge stored in the system. The usage of an abstract, object-centric structure enables not only the application of knowledge in the same situation, but also the transfer to similar environments. Furthermore, the information can be derived from different sensing modalities. The proposed system builds up the representation of manipulation-relevant properties and actions. The properties, which are directly related to the object, are stored in the Object Container. The Functionality Map links the actions with the typical action areas in the environment. We present experimental results on real human actions, showing the quality of the results, that can be obtained with our system.

We propose a system for vision-based estimation of manipulation-relevant properties of objects in natural scenes based on observation of human actions. The system consists of an a-priori (Atlas) knowledge about known generic objects in the scene and classifies the scene into mission relevant objects and background geometry that is important only for collision avoidance. We present the object-centric structure of our system consisting of an Atlas representation and a Working Memory storing the current knowledge about the scene, the manipulated objects and actions applied to them in the local environment. We present experimental results how the system maintains the information in the database and we show the quality of the results that can be obtained with our system.