We present a concept for long-term feedback during robot assisted indoor cycling training. Our feedback model captures different aspects from sport motivation theory. Furthermore, we present our designed measurements to evaluate the robot's persuasiveness and user's compliance. We conducted an intensive 18-day isolation study in two campaigns (e.g. socially assistive robot vs. display instructed, n=16) in cooperation with the German Aerospace Center. The results show that users tend to comply to the robot's instructions and that there is a significant difference in compliance between the two conditions.

The topic of motivation is a crucial issue for various human-robot interaction (HRI) scenarios. Interactional aspects of motivation can be studied in human-human interaction (HHI) and build the basis for modeling a robot's interactional conduct. Using an ethnographic approach we explored the factors relevant in the formation of motivation-relevant processes in an indoor-cycling activity. We propose an interactive, action-based motivation model for HRI that has been implemented in an autonomous robot system and tested during a long-term HRI study. The model is based on micro-analyses of human indoor cycling courses and resulted in an adaption of specific dialog patterns for HRI. A qualitative evaluation - accompanied by a quantitative analysis - demonstrated that the transfer of interaction patterns from HHI to HRI was successful with participants benefitting from the interaction experience (e.g., performance, subjective feeling of being motivated).

Users draw on four sources to judge a robot's competence: (1) the robot's voice, (2) physical appearance of and (3) the interaction experience with the robot but also (4) the relationship between the robot's physical appearance and its conduct. Furthermore, most approaches in social robotics have an outcome-oriented focus and thus use questionnaires to measure a global evaluation of the robot after interaction took place. The present research takes a process-oriented approach to explore the factors relevant in the formation of users' attitudes toward the robot. To do so, an ethnographic approach (Conversation Analysis) was employed to analyze the micro-coordination between user and robot. We report initial findings from a study in which a robot took the role of a fitness instructor. Our results emphasize that the participant judges step-by-step the robot's capabilities and differentiates its competence on two levels regarding to the robot's role: a robot as a (1) social/interactional co-participant and as a (2) fitness instructor.

As a testbed for real-world experimentation on HRI and dynamic interaction models, this paper presents an autonomous robot system acting as guide in a German arts museum. The visitors' evaluation of this system is analyzed using a questionnaire and reveals issues for subsequent analysis of the real-time interaction.

If robot systems are being deployed in real world settings with untrained users who happen to accidentally pass by or could leave at any moment in time, then this places specific demands on the robot system: it needs to secure and maintain the user's engagement. In this, a common and critical problem consists of entering into a `focused encounter'. It requires each interactional partner to closely react upon the other's actions on a very fine-grained level engaging in a stepwise and dynamic process of mutual adjustments. We report initial findings from a study in which we have developed a preliminary, simple solution to this problem inspired by work from Conversation Analysis. Using this as an instrument to explore the impact of a `contingent' (CE) vs. `non-contingent entry' (NCE), we find that users who enter into the interaction in a dynamic and contingent manner show a significantly different way of interacting with the robot than the NCE group.