Nicole Mirnig is a PhD Research Fellow at the Center for Human-Computer Interaction and holds a Master’s Degree in Communication Studies from the University of Salzburg, Austria. She was engaged in the EU-projects IURO (Interactive Urban Robot) and ReMeDi (Remote Medical Diagnostician), focusing on improving human-robot interaction by the means of adequate feedback strategies. In addition, Nicole researched human-robot collaboration in industrial contexts within the Christian Doppler Laboratory “Contextual Interfaces”. In 2013/2014, she spent nine months as a visiting researcher at the A*STAR Institute for Infocomm Research in Singapore, deepening her research in robot feedback. Nicole’s research focus lies in human-robot cooperation, taking into account different factors that foster a positive user experience. Her most recent work is dedicated to systematically researching erroneous robot behaviour. She aims at making robots understand they made a mistake and react accordingly. Another hot topic for Nicole is researching robot humor and how it can be exploited for an enjoyable user experience.
Working Title of Dissertation
Essentials of Robot Feedback
Summary and Expected Results
To enable a future in which humans and social robots successfully coexist and cooperate, humans must be able to understand the actions and intentions of these robots. Better “readable” robots can be achieved through a more sophisticated robot feedback setup. However, this is normally not at the center of attention in many studies but is often merely one variable next to the main research interest. Readability of robots has been one of Nicole’s main research interests so far, since it is still one of the major obstacles of successful HRI. A systematical analysis of existing studies and their design is an important step for a research basis upon which future work can be built. In order to provide such an analysis, a taxonomy of robot feedback is developed, which is itself subject to continuous modification and evolution to keep the basis relevant for future research needs. The major results of this thesis will be twofold: a) a taxonomy for robot feedback will be established and b) studies on robot feedback will be performed, the results of which will be used to refine the proposed taxonomy.