Development of a graphical user interface for a socially interactive robot: A case study evaluation
Bibliographic record
Abstract
In this paper, we present the development of a graphical user interface for a commercially available humanoid robot to study its interaction with children. The interface has been designed to facilitate the overall operation of the robot, and ultimately improve the interaction with children. Four pilot cases are studied to investigate overall effectiveness of the developed robotic setup in interacting and educating children. Case studies included communication with children in university environment, a day nursery, a junior high school, and at the Winnipeg Children's Hospital. Children and their parents/staffs were asked to provide feedback on how they/their children felt about interacting with the robot. Responses were then translated into quantitative measures. The measures received by each group of children and parents/staffs were compared using the ANOVA test. Results failed to reject the null hypothesis of equality in means of all measures. Therefore, the feedback, provided by both groups using questionnaires, was considered very close to each other. In other words, results indicated that children and parents/staffs expressed great interest in use of the developed system, and believed that such a robot could be a helpful tool in child therapy like autism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".