Elaine S. Short

How to train your DragonBot: Socially assistive robots for teaching children about nutrition through play

This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in engagement with the system over the duration of the interaction. We also find evidence of relationship-building between the child and robot, and encouraging trends towards child learning. These results are promising for the use of socially assistive robotic technologies for long-term one-on-one educational interventions for younger children.

Effects of comparative feedback from a Socially Assistive Robot on self-efficacy in post-stroke rehabilitation

We present a study with an autonomous Socially Assistive Robot (SAR) coach that investigates the effect of comparative feedback given by a SAR on the self-efficacy of individuals post-stroke in a seated reaching task. We compare two types of feedback, self-comparative and other-comparative, against a control of no comparative feedback, with 23 participants post-stroke. We find that participants receiving other-comparative feedback have significantly more delay time on the task than participants receiving self or no comparative feedback. In addition, we demonstrate that participants show task performance improvement over time, and provide responses to self-efficacy probes that vary along several dimensions.

A comparison of machine learning techniques for modeling human-robot interaction with children with autism

Several machine learning techniques are used to model the behavior of children with autism interacting with a humanoid robot, comparing a static model to a dynamic model using hand-coded features. Good accuracy (over 80%) is achieved in predicting child vocalizations; directions for future approaches to modeling the behavior of children with autism are suggested.