Abstract. The purpose of this Wizard-of-Oz study was to explore the intuitive verbal and non-verbal goal-directed behavior of naïve participants in an intelligent robotics apartment. Participants had to complete seven mundane tasks, for instance, they were asked to turn on the light. Participants were explicitly instructed to consider nonstandard ways of completing the respective tasks. A multi-method approach revealed that most participants favored speech and interfaces like switches and screens to communicate with the intelligent robotics apartment. However, they required instructions to use the interfaces in order to perceive them as competent targets for human-machine interaction. Hence, first important steps were taken to investigate how to design an intelligent robotics apartment in a user-centered and user-friendly manner.Keywords: Social robot • smart home • human-robot interaction • use-case scenario • usability • intuitive design • user-centered design.

Abstract. Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available.

No abstract

No abstract

In this paper, we present our humanoid robot Meka, participating in a multi party human robot dialogue scenario. Active arbitration of the robot's attention based on multi-modal stimuli is utilised to observe persons which are outside of the robots field of view. We investigate the impact of this attention management and addressee recognition on the robot's capability to distinguish utterances directed at it from communication between humans. Based on the results of a user study, we show that mutual gaze at the end of an utterance, as a means of yielding a turn, is a substantial cue for addressee recognition. Verification of a speaker through the detection of lip movements can be used to further increase precision. Furthermore, we show that even a rather simplistic fusion of gaze and lip movement cues allows a considerable enhancement in addressee estimation, and can be altered to adapt to the requirements of a particular scenario.