This paper presents a novel robotic architecture that is suitable for modular distributed multi-robot systems. The architecture is based on an interface supporting real-time inter-process communication, which allows simple and highly efficient data exchange between the modules. It allows monitoring of the internal system state and easy logging, thus facilitating the module development. The extension to distributed systems is provided through a communication middleware, which enables fast and transparent exchange of data through the network, although without real-time guarantees. The advantages and disadvantages of the architecture are rated using an existing framework for evaluation of robot architectures.

Abstract-This video presents a robot capable of playing pool on a normal sized pool table using two arms. For successfully completing this task several issues need to be addressed, including the perception of relevant environment information, planning of actions and finally an efficient execution. The video outlines how the robot accurately locates the pool table, the balls on the table and the cue and subsequently plans the next shot. In order to improve the stroke speed, an optimization algorithm for the arm configuration is described. Finally, it is shown how all these modules are integrated to achieve a working two-handed robotic pool play.

Abstract-The ability to infer intentions and predict actions enables coordinating of one's own actions with those of another human and allows smooth and intuitive interaction. The aim to achieve equally effective human-robot interactions is a crucial aspect of current robotic studies. Thus, we assume that studying human-human interaction provides valuable insights allowing to implement mutual intention recognition and action prediction in robotic systems. A common scenario of interaction, be it in everyday life or in an industrial setting, is that two or more agents share the same workspace and perform tasks without interference. If humans are involved, the robots should act sufficiently predictable to enable the human to attribute goals and predict motion trajectories. In the present work, we first analyzed how well a human recognizes the goal of another person entering the room, and whether this ability is deteriorated by concealing gaze direction of the other person. In a second setup, the same experiment was repeated by replacing the approaching person with a wheeled robot. On average, the distance at which subjects predicted the goal of the approaching agent was approx. 4 m and depended on subject and goal position, but not on the type of agent. However, goal attribution showed a considerable proportion of errors for the robot (19%), much less for a human with hidden gaze direction (6%), and almost none for a human with visible gaze (1%). Thus, our subjects apparently decided on the goal of the approaching agent without taking into account the reliability of directional cues, thus resulting in more errors. In a human-robot setting, such wrong predictions about robotic behavior may easily lead to dangerous situations. For smooth and safe interaction, it is therefore important to ameliorate the predictability of robotic actions.

SUMMARYRobotic systems operating in the real-world have to cope with unforeseen events by determining appropriate decisions based on noisy or partial knowledge. In this respect high functional robots are equipped with many sensors and actuators and run multiple processing modules in parallel. The resulting complexity is even further increased in case of cooperative multi-robot systems, since mechanisms for joint operation are needed. In this paper a complete and modular framework that handles this complexity in multi-robot systems is presented. It provides efficient exchange of generated data as well as a generic scheme for task execution and robot coordination.

Abstract-The overall performance of a robotic system is commonly expressed by a single scenario-specific metric which is supposed to be optimized. However, the metric describing the performance of a single subtask within a scenario may be different. Nevertheless, the scenario performance is most likely dependent on the subtask performances but a mutual transformation is not straightforward in general, especially in complex robotic systems. This leads to what we call the common pricing problem, i.e. the problem to determine the functional relationship among a set of different performance criteria and then account for this relationship in the various optimizations throughout all system layers. In this paper we present an approach to first learn a probabilistic model of the metric interdependencies, and thereafter utilize this model for performance estimation and optimal task parameterization during planning and execution respectively. The proposed method is validated in a simulation.