Language can be a useful tool for social robots as part of their repertoire of social engagement. This late breaking report outlines preliminary studies into how a child can teach a robot lexicons for colors and color relations. The robot used is a minimal social robot, made from cardboard and foam, that interacts with the children through a simple color naming game. Distributed, non-parametric lexicons similar to those used in previous language learning robot studies are used to store links between words and colors. We visually present the resulting lexicons and highlight the issues that have arisen from this preliminary study and how they can be resolved for future studies. The results of this study indicate that children can teach a social robot lexicons, allowing the children and robot to develop a shared set of symbols for color.

Robots that facilitate touch by children have special requirements in terms of safety and robustness, but little is known about how and when children actually use touch with robots. Tools and techniques are required to sense the variety of children's touch and to interpret the volumes of data generated. This explorative user study investigated children's patterns of touch during game play with a robot. We examined where the children touch the robot and their patterns of touch over time, using a raster-based visualisation of each child's time series of touches, recording patterns of touch across different games and children. We found that children readily engage with the robot, in particular spontaneously touching the robot's hands more than any other area. This user study and the tools developed may aid future designs of robots to autonomously detect when they have been touched.

An important challenge in Human-Robot Interaction (HRI) is the analysis and interpretation of large volumes of data to inform the design process within a complex, multi-faceted research space. In this study, we explore how data visualization techniques can contribute to HRI methodology, particularly in terms of linking qualitative and quantitative analysis methods. Specifically, we present a case study demonstrating the visualization of touch data to identify potential patterns of interaction with a social robot intended for deployment in preschool classrooms.

Pretotyping is a set of techniques, tools, and metrics for gauging the interest in a product, prior to full-scale development [1]. This late breaking report describes a pretotyping case study of an ethnodroid - a robot that functions as an ethnographer - intended to engage with young children and record their learning progress. The central requirement for the project is that the robot will be able to interact socially with children aged 1-6 years in tablet-based tasks. We developed a simple robot made of MDF (thick cardboard), added tablets for the face and torso, and controlled a scripted interaction using Wizard of Oz (WoZ). Children's engagement with the robot was tested in an early learning centre which provided a relatively structured environment (“in the lab”) and at a science fair which provided a relatively unconstrained setting (“in the wild”). The rapid testing revealed distinct effects in the children's attitudes and behaviors in the two user contexts and provided insights into form, sensors and analyses for the design process.