Contingency allows the robot to spot the tutor and to learn from interaction
Aiming at artificial system learning from a human tutor elicit tutoring behavior, which we implemented on the robotic platform iCub. For the evaluation of the system with users, we considered a contingency module that is developed to elicit tutoring behavior, which we then evaluate by implementing this module on the robotic platform iCub and within an interaction with the users. For the evaluation of our system, we consider not only the participant's behavior but also the system's log-files as dependent variables (as it was suggested in  for the improvement of HRI design). We further applied Sequential Analysis as a qualitative method that provides micro-analytical insights into the sequential structure of the interaction. This way, we are able to investigate a closer interrelationship between robot's and tutor's actions and how they respond to each other. We focus on two cases: In the first case, the system module was reacting to the interaction partner appropriately; in the second case, the contingency module failed to spot the tutor. We found that the contingency module enables the robot to engage in an interaction with the human tutor who orients to the robot's conduct as appropriate and responsive. In contrast, when the robot did not engage in an appropriate responsive interaction, the tutor oriented more towards the object while gazing less at the robot.