Research of tutoring in parent-infant interaction has shown that tutors – when presenting some action – modify both their verbal and manual performance for the learner (‘motherese’, ‘motionese’). Investigating the sources and effects of the tutors’ action modifications, we suggest an interactional account of ‘motionese’. Using video-data from a semi-experimental study in which parents taught their 8- to 11-month old infants how to nest a set of differently sized cups, we found that the tutors’ action modifications (in particular: high arches) (...) functioned as an orienting device to guide the infant’s visual attention (gaze). Action modification and the recipient’s gaze can be seen to have a reciprocal sequential relationship and to constitute a constant loop of mutual adjustments. Implications are discussed for developmental research and for robotic ‘Social Learning’. We argue that a robot system could use on-line feedback strategies (e.g. gaze) to pro-actively shape a tutor’s action presentation as it emerges. (shrink)

The paper investigates the effects of a humanoid robot’s online feedback during a tutoring situation in which a human demonstrates how to make a frog jump across a table. Motivated by micro-analytic studies of adult-child-interaction, we investigated whether tutors react to a robot’s gaze strategies while they are presenting an action. And if so, how they would adapt to them. Analysis reveals that tutors adjust typical “motionese” parameters. We argue that a robot – when using adequate online feedback strategies – (...) has at its disposal an important resource with which it could proactively shape the tutor’s presentation and help generate the input from which it would benefit most. These results advance our understanding of robotic “Social Learning” in that they suggest a paradigm shift towards considering human and robot as one interational learning system. Keywords: human-robot-interaction; feedback; adaptation; multimodality; gaze; conversation analysis; social learning; pro-active robot conduct. (shrink)

Studying teaching behavior in controlled conditions is difficult. It seems intuitive that a human learner might have trouble reliably recreating response patterns over and over in interaction. A robot would be the perfect tool to study teaching behavior because its actions can be well controlled and described. However, due to the interactive nature of teaching, developing such a robot is not an easy task. As we will show in this review, respective studies require certain robot appearances and behaviors. These mainly (...) should induce teaching behavior in humans, be interactive, match the study design, and be realizable in terms of effort. We discuss how remote controlling of the robot or simulating robot capabilities is used as an option. With this review, we introduce the field of research on studying human teaching behavior with robots as a tool in the experimental design. We will provide a structured overview of existing work, and identify main challenges of employing robots in such studies. (shrink)

Research in social robotics is commonly focused on designing robots that imitate human behavior. While this might increase a user’s satisfaction and acceptance of robots at first glance, it does not automatically aid a non-expert user in naturally interacting with robots, and might hurt their ability to correctly anticipate a robot’s capabilities. We argue that a faulty mental model, that the user has of the robot, is one of the main sources of confusion. In this work, we investigate how communicating (...) technical concepts of robotic systems to users affect their mental models, and how this can increase the quality of human-robot interaction. We conducted an online study and investigated possible ways of improving users’ mental models. Our results underline that communicating technical concepts can form an improved mental model. Consequently, we show the importance of consciously designing robots that express their capabilities and limitations. (shrink)

How do we switch between “playing along” and treating robots as technical agents? We propose interaction breakdowns to help solve this “social artifact puzzle”: Breaks cause changes from fluid interaction to explicit reasoning and interaction with the raw artifact. These changes are closely linked to understanding the technical architecture and could be used to design better human–robot interaction (HRI).